In [ ]:
num_iterations = 200
print("The number of iterations is: ", num_iterations)
The number of iterations is:  200
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt


def data(N,sigma):   
    w = np.ones(10)/np.sqrt(10)   
    w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)   
    w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)   
    x = np.zeros((4,10))   
    x[1,:] = x[0,:] + sigma*w1   
    x[2,:] = x[0,:] + sigma*w2   
    x[3,:] = x[2,:] + sigma*w1   
    X1 = x + sigma*matlib.repmat(w,4,1)/2   
    X2 = x - sigma*matlib.repmat(w,4,1)/2   
    X1 = matlib.repmat(X1,2*N,1)   
    X2 = matlib.repmat(X2,2*N,1)   
    X = np.concatenate((X1, X2), axis=0)   
    Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)   
    Z = np.random.permutation(16*N)   
    Z = Z[:N]   
    X = X[Z,:]   
    X = X + 0.2*sigma*np.random.randn(N,10)   
    Y = Y[Z]

    return X, Y

# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-2, 1, 20)  # Logarithmically spaced values between 0.01 and 10

# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 0.5

# Task 2b-d: Training, Testing, and Repeating the Experiment
#num_iterations = 100
for i in range(num_iterations):

    # Generate data
    X_train, y_train = data(100,sigma)
    X_test, y_test = data(1000, sigma)

    for lam in lambda_values:
        
        # Train SVM
        svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
        param = svm_parameter(f'-t 0 -c {lam}')
        model = svm_train(svm_problem_setup, param)
        
        # Predict on training and test data
        i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
        i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
        
        # Calculate errors
        training_errors.append(100 - train_accuracy[0])  # Convert to error percentage
        test_errors.append(100 - test_accuracy[0])  # Convert to error percentage

# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)

avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)

lambda_values_log = np.log10(lambda_values)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')

plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.01,10) @ σ = 0.5')
plt.legend()
plt.show()
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.894241, rho = 0.872642
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -1.270081, rho = 0.816829
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.793336, rho = 0.736518
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -2.510079, rho = 0.622441
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -3.466723, rho = 0.456901
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 55% (55/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -4.688957, rho = 0.218779
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 76% (76/100) (classification)
Accuracy = 74% (740/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.138730, rho = -0.043379
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.782803, rho = -0.122397
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.740000
obj = -9.737874, rho = -0.114800
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.653751
obj = -12.014190, rho = -0.055246
nSV = 69, nBSV = 62
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.562034
obj = -14.712235, rho = -0.013484
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.481719
obj = -17.850051, rho = 0.005457
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.407737
obj = -21.516851, rho = -0.034123
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.339878
obj = -26.038311, rho = -0.106560
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.284277
obj = -31.580581, rho = -0.104549
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.243334
obj = -38.019377, rho = -0.091532
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.202632
obj = -45.293761, rho = -0.029927
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.168929
obj = -54.261038, rho = -0.062588
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.141692
obj = -65.156725, rho = -0.180296
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.119350
obj = -77.476837, rho = -0.259244
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.948416, rho = 0.812954
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 54.3% (543/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.344330, rho = 0.730943
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 54.3% (543/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.892535, rho = 0.612976
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 54.3% (543/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.637034, rho = 0.443285
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 54.3% (543/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.616781, rho = 0.199193
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 69% (69/100) (classification)
Accuracy = 67.7% (677/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.837436, rho = -0.151921
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 87% (87/100) (classification)
Accuracy = 92.2% (922/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.932892
obj = -6.261974, rho = -0.276373
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.935809, rho = -0.200697
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.768030
obj = -9.862578, rho = -0.211248
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.662336
obj = -12.146676, rho = -0.172374
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.570180
obj = -14.869296, rho = -0.146401
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.478976
obj = -18.199165, rho = -0.109274
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.411964
obj = -22.325856, rho = -0.071661
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.356517
obj = -27.083053, rho = -0.108234
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.298400
obj = -32.770027, rho = -0.143746
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.250586
obj = -39.426365, rho = -0.166812
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.217015
obj = -46.924552, rho = -0.116885
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.175244
obj = -55.321719, rho = -0.141231
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*....*
optimization finished, #iter = 506
nu = 0.141854
obj = -65.666426, rho = -0.215708
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.122682
obj = -78.181472, rho = -0.213607
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.780000
obj = -0.759544, rho = 0.905397
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.780000
obj = -1.079664, rho = 0.863918
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.780000
obj = -1.526349, rho = 0.805126
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.780000
obj = -2.140345, rho = 0.719409
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.780000
obj = -2.964493, rho = 0.596062
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.780000
obj = -4.027802, rho = 0.418254
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 71% (71/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -5.304494, rho = 0.163188
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 91% (91/100) (classification)
Accuracy = 81.3% (813/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.736404
obj = -6.677423, rho = 0.010425
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.656987
obj = -8.206756, rho = 0.026977
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.555385
obj = -9.967226, rho = 0.038011
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.488294
obj = -11.899243, rho = 0.189074
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.405242
obj = -13.867543, rho = 0.246108
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.324758
obj = -16.042484, rho = 0.256644
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.266024
obj = -18.518560, rho = 0.292320
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.215031
obj = -21.141831, rho = 0.344646
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.171222
obj = -23.949243, rho = 0.386644
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.132300
obj = -27.167771, rho = 0.387980
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.103808
obj = -31.110985, rho = 0.458295
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.082365
obj = -35.658619, rho = 0.521824
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.066658
obj = -41.150112, rho = 0.550173
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.934661, rho = -0.922823
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.3% (533/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.328482, rho = -0.888985
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.3% (533/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.877887, rho = -0.840311
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.3% (533/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.632827, rho = -0.770296
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.3% (533/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.645618, rho = -0.669582
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.951109, rho = -0.524710
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 74% (74/100) (classification)
Accuracy = 74.8% (748/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.957032
obj = -6.515934, rho = -0.321408
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.866012
obj = -8.368515, rho = -0.281973
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.786355
obj = -10.632172, rho = -0.219484
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.705741
obj = -13.280602, rho = -0.254162
nSV = 73, nBSV = 66
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.620000
obj = -16.445226, rho = -0.169077
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.542998
obj = -20.075948, rho = -0.105843
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.458270
obj = -24.076281, rho = -0.144930
nSV = 49, nBSV = 40
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.383723
obj = -28.987748, rho = -0.193480
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.319162
obj = -35.121374, rho = -0.094988
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.261534
obj = -42.811443, rho = -0.113216
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.223766
obj = -52.795724, rho = -0.258573
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.193583
obj = -65.104725, rho = -0.264434
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.165780
obj = -80.199580, rho = -0.351939
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 256
nu = 0.143303
obj = -97.849654, rho = -0.302076
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.840074, rho = -0.939555
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.195837, rho = -0.913053
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.694148, rho = -0.874931
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -2.383142, rho = -0.820094
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -3.316701, rho = -0.741214
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -4.540553, rho = -0.627749
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 59% (59/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.054720, rho = -0.464536
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 90% (90/100) (classification)
Accuracy = 77.6% (776/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.841330
obj = -7.729993, rho = -0.256635
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.746244
obj = -9.554298, rho = -0.190284
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.644040
obj = -11.726645, rho = -0.170471
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.560317
obj = -14.236850, rho = -0.254691
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.459922
obj = -17.228126, rho = -0.284998
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.386644
obj = -21.004899, rho = -0.261024
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.327099
obj = -25.739756, rho = -0.302342
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.282056
obj = -31.702616, rho = -0.252171
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.243085
obj = -38.626377, rho = -0.286968
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.205021
obj = -46.792205, rho = -0.170775
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.174037
obj = -56.267062, rho = -0.155347
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.140773
obj = -68.282727, rho = -0.208296
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.118374
obj = -84.738231, rho = -0.404139
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.909809, rho = 0.836987
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.289674, rho = 0.765514
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.815734, rho = 0.662704
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.530322, rho = 0.514817
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.471065, rho = 0.302088
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 62% (62/100) (classification)
Accuracy = 56.2% (562/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.643939, rho = -0.003911
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 85% (85/100) (classification)
Accuracy = 83.5% (835/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.901669
obj = -6.000473, rho = -0.201868
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.821057
obj = -7.581915, rho = -0.207237
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.731621
obj = -9.429145, rho = -0.090792
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.637213
obj = -11.525950, rho = -0.129124
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.545823
obj = -13.980868, rho = -0.073426
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.458885
obj = -16.930453, rho = -0.152597
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.385013
obj = -20.405449, rho = -0.162059
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.326267
obj = -24.517639, rho = -0.150989
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.271288
obj = -29.483777, rho = -0.149807
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.225999
obj = -35.507207, rho = -0.185487
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.195609
obj = -42.394652, rho = -0.136595
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.164290
obj = -49.359129, rho = -0.122313
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.136227
obj = -56.366992, rho = -0.111068
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.104493
obj = -63.596241, rho = -0.033529
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.916683, rho = -0.934706
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.303897, rho = -0.906078
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.845163, rho = -0.864898
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.591214, rho = -0.805662
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.597059, rho = -0.720455
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.904638, rho = -0.597888
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 59% (59/100) (classification)
Accuracy = 56.4% (564/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.497335, rho = -0.421583
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 87.1% (871/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -8.246447, rho = -0.295005
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.808124
obj = -10.132165, rho = -0.194577
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.685783
obj = -12.282934, rho = -0.158121
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.575159
obj = -14.884348, rho = -0.200747
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.490737
obj = -17.953213, rho = -0.227047
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.407676
obj = -21.646414, rho = -0.284420
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.337897
obj = -26.222694, rho = -0.302113
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.287292
obj = -32.045853, rho = -0.229780
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.247966
obj = -38.919225, rho = -0.355756
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.209298
obj = -46.597206, rho = -0.393685
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 360
nu = 0.171726
obj = -56.018526, rho = -0.398909
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.140570
obj = -68.398866, rho = -0.384777
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.125344
obj = -83.573273, rho = -0.290513
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.927941, rho = -0.907083
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.314578, rho = -0.866344
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.849119, rho = -0.807742
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.573301, rho = -0.723446
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.522451, rho = -0.602191
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 59% (59/100) (classification)
Accuracy = 52.4% (524/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.696260, rho = -0.427772
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 83% (83/100) (classification)
Accuracy = 80.1% (801/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.916624
obj = -6.018205, rho = -0.310501
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -7.565467, rho = -0.277969
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.732786
obj = -9.348153, rho = -0.180532
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.635640
obj = -11.380150, rho = -0.105362
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.529927
obj = -13.850504, rho = -0.143259
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.467033
obj = -16.550410, rho = -0.084122
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.385120
obj = -19.502439, rho = -0.040653
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.321943
obj = -22.776121, rho = 0.036404
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.260776
obj = -26.554721, rho = 0.090139
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.212261
obj = -30.622655, rho = 0.051763
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.172274
obj = -35.254337, rho = -0.047647
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.134883
obj = -40.343656, rho = -0.015500
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.105588
obj = -46.762458, rho = 0.022949
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.084267
obj = -55.302640, rho = 0.080553
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.948418, rho = -0.883717
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.344334, rho = -0.832733
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.892543, rho = -0.759395
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.637052, rho = -0.653901
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.616817, rho = -0.502155
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 59% (59/100) (classification)
Accuracy = 55.2% (552/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.837513, rho = -0.283874
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 88.2% (882/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.924171
obj = -6.257282, rho = -0.153905
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.848765
obj = -7.959231, rho = -0.090605
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.764801
obj = -9.927219, rho = -0.022228
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.669207
obj = -12.181348, rho = -0.046503
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.567510
obj = -14.890042, rho = -0.006445
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.486208
obj = -18.237741, rho = -0.002157
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.413793
obj = -22.059228, rho = -0.019754
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.346721
obj = -26.965106, rho = -0.043190
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.295563
obj = -32.778280, rho = -0.011321
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.253864
obj = -39.616180, rho = -0.076659
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.212160
obj = -47.393598, rho = -0.101700
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.175824
obj = -56.795206, rho = -0.123676
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 219
nu = 0.146618
obj = -67.937544, rho = -0.120196
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.128610
obj = -80.654166, rho = -0.144184
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.915296, rho = -0.939027
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.301026, rho = -0.912293
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.839223, rho = -0.873838
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.578924, rho = -0.818523
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.571629, rho = -0.738954
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.852019, rho = -0.624499
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 75% (75/100) (classification)
Accuracy = 70% (700/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.388461, rho = -0.473279
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 91% (91/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.867682
obj = -8.155682, rho = -0.421749
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.772979
obj = -10.245545, rho = -0.424881
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.693108
obj = -12.692485, rho = -0.506971
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.600000
obj = -15.517941, rho = -0.420187
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.502441
obj = -18.881726, rho = -0.348684
nSV = 56, nBSV = 48
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.426587
obj = -22.970597, rho = -0.287179
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.357157
obj = -28.093121, rho = -0.261932
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.303520
obj = -34.678873, rho = -0.185043
nSV = 32, nBSV = 29
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.257527
obj = -42.873623, rho = -0.173817
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.221990
obj = -52.771545, rho = -0.269947
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.184931
obj = -66.056904, rho = -0.301927
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.160425
obj = -84.158755, rho = -0.307251
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 264
nu = 0.142934
obj = -107.397512, rho = -0.304593
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.932148, rho = -0.914794
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.323283, rho = -0.877436
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.867131, rho = -0.823698
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.610569, rho = -0.746398
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.599565, rho = -0.635207
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 55% (55/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.855818, rho = -0.475263
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 83% (83/100) (classification)
Accuracy = 81.4% (814/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.341966, rho = -0.315748
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.030328, rho = -0.296815
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.769159
obj = -9.952788, rho = -0.265252
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.671453
obj = -12.267780, rho = -0.277554
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.579430
obj = -14.915916, rho = -0.299330
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.490426
obj = -18.044064, rho = -0.298521
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.411144
obj = -21.874181, rho = -0.296040
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.354013
obj = -26.210630, rho = -0.286874
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.296901
obj = -31.083845, rho = -0.313256
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.241757
obj = -36.895500, rho = -0.317896
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.198421
obj = -43.694681, rho = -0.363681
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.167695
obj = -51.850784, rho = -0.448712
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.134875
obj = -60.500044, rho = -0.477883
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.108280
obj = -71.884556, rho = -0.443700
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.897147, rho = -0.936598
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.276087, rho = -0.908800
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.805765, rho = -0.868813
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.535794, rho = -0.811294
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.519930, rho = -0.728556
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.799051, rho = -0.609541
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 65% (65/100) (classification)
Accuracy = 61.1% (611/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.356543, rho = -0.438344
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 91% (91/100) (classification)
Accuracy = 90.8% (908/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.888975
obj = -8.052154, rho = -0.294232
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.777408
obj = -9.944326, rho = -0.228332
nSV = 81, nBSV = 75
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.663451
obj = -12.250412, rho = -0.190624
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.575234
obj = -15.087815, rho = -0.136746
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.489244
obj = -18.363353, rho = -0.131503
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.412476
obj = -22.399268, rho = -0.085199
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.365588
obj = -26.983772, rho = -0.221089
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.300122
obj = -31.813817, rho = -0.276481
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.244222
obj = -37.628723, rho = -0.266493
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.199987
obj = -45.056266, rho = -0.283644
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.162633
obj = -54.857666, rho = -0.315324
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.138468
obj = -67.290070, rho = -0.378727
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*....*
optimization finished, #iter = 563
nu = 0.115400
obj = -83.526903, rho = -0.463148
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -0.943413, rho = 0.820812
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.333978, rho = 0.742247
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.871115, rho = 0.629235
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -2.592713, rho = 0.466674
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.525075, rho = 0.232837
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 66% (66/100) (classification)
Accuracy = 63.6% (636/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.647685, rho = -0.103526
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 87.6% (876/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.920000
obj = -5.901816, rho = -0.205669
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 97% (97/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.825874
obj = -7.318062, rho = -0.216088
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.713909
obj = -8.946551, rho = -0.183119
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.605011
obj = -10.837280, rho = -0.216319
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.505363
obj = -13.180487, rho = -0.265816
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.429478
obj = -16.015038, rho = -0.251630
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.378526
obj = -19.298335, rho = -0.302634
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.308033
obj = -22.837610, rho = -0.289786
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.255647
obj = -27.103859, rho = -0.285095
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.219604
obj = -31.871619, rho = -0.153628
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.174898
obj = -36.658237, rho = -0.177715
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.139485
obj = -42.838159, rho = -0.125289
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.113816
obj = -49.377809, rho = -0.111099
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.092136
obj = -57.169360, rho = -0.152877
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.919273, rho = -0.916418
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.309256, rho = -0.879771
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.856250, rho = -0.827057
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.614156, rho = -0.751230
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.644528, rho = -0.642157
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -5.002857, rho = -0.485260
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 60% (60/100) (classification)
Accuracy = 61.4% (614/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.700566, rho = -0.259573
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 85% (85/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -8.670170, rho = -0.093942
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820244
obj = -10.972033, rho = -0.031003
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.725395
obj = -13.739080, rho = -0.064363
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.633611
obj = -17.006662, rho = -0.033203
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.555815
obj = -20.957052, rho = -0.067559
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.480289
obj = -25.404861, rho = -0.033760
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.403489
obj = -30.513705, rho = 0.076147
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.335741
obj = -36.784304, rho = 0.027804
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.289430
obj = -44.373710, rho = 0.011523
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.239168
obj = -52.486973, rho = 0.037833
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.198310
obj = -62.150357, rho = 0.093444
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.163566
obj = -73.087115, rho = 0.134212
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.134537
obj = -86.010654, rho = -0.002090
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.908505, rho = -0.915134
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.286975, rho = -0.877925
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.810149, rho = -0.824401
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.518766, rho = -0.747410
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.447154, rho = -0.636661
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 62% (62/100) (classification)
Accuracy = 55.4% (554/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.594462, rho = -0.477356
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 89% (89/100) (classification)
Accuracy = 81.8% (818/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.901204
obj = -5.878065, rho = -0.366041
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.793901
obj = -7.394270, rho = -0.377272
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 94% (94/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700836
obj = -9.270662, rho = -0.399017
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.616671
obj = -11.512436, rho = -0.363753
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.548713
obj = -13.992746, rho = -0.324167
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.459126
obj = -16.904493, rho = -0.259410
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.389611
obj = -20.312897, rho = -0.333839
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.317518
obj = -24.505047, rho = -0.352847
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.268463
obj = -29.636172, rho = -0.289540
nSV = 32, nBSV = 22
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.225275
obj = -36.035778, rho = -0.219425
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.190370
obj = -43.803454, rho = -0.172247
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.166057
obj = -52.805046, rho = -0.134617
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.135957
obj = -63.200853, rho = -0.206636
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.111401
obj = -76.189255, rho = -0.252563
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.915633, rho = -0.922207
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.301727, rho = -0.888057
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.840672, rho = -0.838976
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.581923, rho = -0.768375
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.577834, rho = -0.666820
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.864858, rho = -0.520737
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 72% (72/100) (classification)
Accuracy = 69.6% (696/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.415026, rho = -0.310604
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 92% (92/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.873944
obj = -8.187349, rho = -0.217850
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.778333
obj = -10.266341, rho = -0.185551
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.685620
obj = -12.785867, rho = -0.149603
nSV = 70, nBSV = 68
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.593441
obj = -15.767068, rho = -0.128467
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.514560
obj = -19.444622, rho = -0.087827
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.442560
obj = -23.754703, rho = -0.077962
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.383009
obj = -28.616835, rho = -0.043197
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.311565
obj = -34.445521, rho = -0.075147
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.257916
obj = -42.201526, rho = -0.074545
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.217055
obj = -51.998937, rho = -0.160740
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.190537
obj = -64.645146, rho = -0.056039
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.167158
obj = -78.786433, rho = -0.103113
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.136602
obj = -96.690795, rho = -0.092781
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -0.951128, rho = 0.862482
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.349941, rho = 0.802188
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.904145, rho = 0.715457
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.661057, rho = 0.590700
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.666488, rho = 0.411242
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.940287, rho = 0.153101
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 82% (82/100) (classification)
Accuracy = 80.1% (801/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.975475
obj = -6.415958, rho = -0.198201
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 93% (93/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.867212
obj = -8.113447, rho = -0.221286
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.774278
obj = -10.195696, rho = -0.201015
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 94% (94/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.688494
obj = -12.654991, rho = -0.124763
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.600000
obj = -15.412402, rho = -0.090777
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.505791
obj = -18.572245, rho = -0.072711
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.422640
obj = -22.364879, rho = -0.058694
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.349298
obj = -27.115114, rho = -0.094310
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.299106
obj = -32.818314, rho = -0.048332
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.257590
obj = -39.480862, rho = 0.021349
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.208442
obj = -47.157402, rho = 0.007902
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.175323
obj = -57.158313, rho = 0.115336
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.148595
obj = -68.225375, rho = 0.272997
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.120738
obj = -81.812410, rho = 0.333315
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.936552, rho = 0.887596
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.332396, rho = 0.838313
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.885986, rho = 0.767421
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.649583, rho = 0.665447
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.680289, rho = 0.518763
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -5.022847, rho = 0.307764
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 68% (68/100) (classification)
Accuracy = 66.9% (669/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.664246, rho = 0.004254
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.890977
obj = -8.574703, rho = -0.035475
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -10.813830, rho = -0.023580
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.721804
obj = -13.366517, rho = 0.001972
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.639021
obj = -16.294640, rho = 0.040611
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.527359
obj = -19.691750, rho = 0.011687
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.452288
obj = -23.735186, rho = -0.000398
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.379348
obj = -28.409688, rho = 0.022010
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.312994
obj = -33.949141, rho = 0.019075
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.260557
obj = -40.570545, rho = -0.023237
nSV = 32, nBSV = 22
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.213211
obj = -49.068758, rho = -0.030309
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.182195
obj = -59.396177, rho = -0.018693
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.152500
obj = -72.389646, rho = -0.091959
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.**.*
optimization finished, #iter = 244
nu = 0.128427
obj = -87.462564, rho = -0.141857
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.947640, rho = -0.895386
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.342724, rho = -0.849518
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.889213, rho = -0.783540
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.630162, rho = -0.688633
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.602561, rho = -0.552114
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 60% (60/100) (classification)
Accuracy = 56.1% (561/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.808014, rho = -0.355738
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 86% (86/100) (classification)
Accuracy = 86.8% (868/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.202274, rho = -0.221389
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.842411
obj = -7.840816, rho = -0.177509
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.759562
obj = -9.765987, rho = -0.082100
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.663548
obj = -11.937940, rho = -0.002087
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.560000
obj = -14.454393, rho = -0.032231
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.484807
obj = -17.318549, rho = -0.113926
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.404478
obj = -20.563268, rho = -0.053014
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.330622
obj = -24.250621, rho = -0.013268
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.271307
obj = -28.458587, rho = 0.025069
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.225141
obj = -33.287042, rho = 0.089103
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.179292
obj = -39.174391, rho = 0.008648
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.150527
obj = -46.111240, rho = -0.126139
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.119449
obj = -54.057554, rho = -0.145275
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.100263
obj = -64.039486, rho = -0.039883
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.951108, rho = 0.878604
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.349899, rho = 0.825379
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.904058, rho = 0.748816
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.660878, rho = 0.638684
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.666118, rho = 0.480265
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.939522, rho = 0.252388
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 82% (82/100) (classification)
Accuracy = 83.5% (835/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.963311
obj = -6.417420, rho = -0.020977
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.876975
obj = -8.082845, rho = -0.034364
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780520
obj = -10.017370, rho = -0.047748
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.677568
obj = -12.265088, rho = -0.068061
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.573479
obj = -14.893945, rho = -0.069354
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.492801
obj = -17.986039, rho = -0.103276
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.404504
obj = -21.781474, rho = -0.126719
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.344427
obj = -26.364478, rho = -0.128870
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.294528
obj = -31.714196, rho = -0.060440
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.247920
obj = -37.686176, rho = -0.139902
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.208129
obj = -44.384955, rho = -0.080981
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.173201
obj = -51.355968, rho = 0.006753
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.136360
obj = -58.584127, rho = 0.015455
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.115710
obj = -66.193899, rho = -0.139974
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -0.822063, rho = 0.921161
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.171183, rho = 0.886595
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -1.661280, rho = 0.836872
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -2.341234, rho = 0.765349
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -3.267531, rho = 0.662466
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -4.492817, rho = 0.514929
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 59% (59/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -6.033630, rho = 0.302249
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 79% (79/100) (classification)
Accuracy = 73% (730/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820569
obj = -7.799975, rho = 0.083431
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 93% (93/100) (classification)
Accuracy = 91.8% (918/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -9.705318, rho = 0.049167
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.673299
obj = -11.738314, rho = 0.036218
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.560254
obj = -13.972838, rho = 0.033274
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.469407
obj = -16.623427, rho = 0.051973
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.388800
obj = -19.517985, rho = 0.000002
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.310562
obj = -23.112665, rho = -0.005439
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.262511
obj = -27.444393, rho = -0.002972
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.210744
obj = -32.500175, rho = 0.005364
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.174329
obj = -38.943419, rho = 0.083773
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.149139
obj = -46.740767, rho = 0.334610
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.125490
obj = -54.001594, rho = 0.415480
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.098248
obj = -62.748468, rho = 0.454458
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -0.844175, rho = -0.959209
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.204322, rho = -0.941324
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.711706, rho = -0.915598
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.419472, rho = -0.878591
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.391873, rho = -0.825360
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.696094, rho = -0.748789
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -6.376557, rho = -0.638645
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 65% (65/100) (classification)
Accuracy = 59.2% (592/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.389109, rho = -0.480209
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 85% (85/100) (classification)
Accuracy = 88.6% (886/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -10.640803, rho = -0.337306
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.726728
obj = -13.130775, rho = -0.226826
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.623821
obj = -15.881745, rho = -0.145845
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.523121
obj = -19.051561, rho = -0.119951
nSV = 56, nBSV = 48
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.436226
obj = -22.877514, rho = -0.078588
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.364031
obj = -27.542476, rho = -0.039894
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.305799
obj = -32.813183, rho = -0.090538
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.252707
obj = -39.261326, rho = -0.058590
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.212573
obj = -46.935468, rho = -0.051209
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.171397
obj = -56.121124, rho = -0.063063
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.146583
obj = -67.440955, rho = -0.102768
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.123197
obj = -79.584427, rho = -0.334797
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.949629, rho = 0.868396
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.346839, rho = 0.810695
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.897728, rho = 0.727694
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.647779, rho = 0.608302
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.639014, rho = 0.436562
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 56% (56/100) (classification)
Accuracy = 54.6% (546/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.883440, rho = 0.189522
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 85% (85/100) (classification)
Accuracy = 86% (860/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.335233, rho = -0.034594
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.864397
obj = -8.032438, rho = -0.010408
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -10.086280, rho = 0.044315
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.673148
obj = -12.578690, rho = 0.045452
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.587140
obj = -15.477269, rho = 0.000804
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.508217
obj = -18.806301, rho = 0.026262
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.421986
obj = -22.914420, rho = 0.033475
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.357722
obj = -27.881814, rho = 0.000370
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.317898
obj = -33.553720, rho = -0.082893
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.263497
obj = -39.497666, rho = -0.113962
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
.*.....*
optimization finished, #iter = 697
nu = 0.208442
obj = -46.746625, rho = -0.114686
nSV = 27, nBSV = 16
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.171026
obj = -56.648762, rho = -0.064427
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.**.*
optimization finished, #iter = 163
nu = 0.151445
obj = -67.262053, rho = 0.005784
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 390
nu = 0.121094
obj = -79.451740, rho = 0.026760
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.913529, rho = -0.912315
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.297371, rho = -0.873869
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.831658, rho = -0.818567
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.563272, rho = -0.739018
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.539242, rho = -0.624590
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.785006, rho = -0.459992
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 70% (70/100) (classification)
Accuracy = 62.2% (622/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.249801, rho = -0.223225
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -7.827456, rho = -0.136751
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.755029
obj = -9.661119, rho = -0.134966
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.656321
obj = -11.824890, rho = -0.102499
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.568511
obj = -14.278193, rho = -0.074496
nSV = 58, nBSV = 56
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.473317
obj = -17.013858, rho = -0.089730
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.393157
obj = -20.228235, rho = -0.091104
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.322528
obj = -23.998162, rho = -0.113665
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.265880
obj = -28.629006, rho = -0.105747
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.220399
obj = -34.333306, rho = -0.178093
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.182708
obj = -41.527547, rho = -0.225130
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.152112
obj = -50.646870, rho = -0.267183
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.129590
obj = -61.535898, rho = -0.324053
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.106504
obj = -75.320762, rho = -0.306961
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -0.895937, rho = 0.891753
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -1.273584, rho = 0.844293
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -1.800586, rho = 0.776023
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -2.525078, rho = 0.677820
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -3.497756, rho = 0.536560
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -4.753170, rho = 0.333365
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 63% (63/100) (classification)
Accuracy = 64.3% (643/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -6.261610, rho = 0.041079
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 85% (85/100) (classification)
Accuracy = 88.1% (881/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.874359
obj = -7.870381, rho = -0.145078
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.774152
obj = -9.652452, rho = -0.122320
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.657341
obj = -11.658249, rho = -0.144617
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.552738
obj = -14.009290, rho = -0.143940
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.466192
obj = -16.845417, rho = -0.107807
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.389268
obj = -20.061268, rho = -0.126570
nSV = 41, nBSV = 38
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.331218
obj = -23.694549, rho = -0.086859
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.268852
obj = -27.400929, rho = -0.044424
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.216489
obj = -31.628017, rho = -0.049353
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.173591
obj = -36.751078, rho = -0.084191
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.141030
obj = -42.613089, rho = -0.154796
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.112941
obj = -49.341214, rho = -0.223723
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*....*.*
optimization finished, #iter = 571
nu = 0.090184
obj = -57.501067, rho = -0.235833
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.936654, rho = -0.927193
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.332606, rho = -0.895270
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.886421, rho = -0.849352
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.650484, rho = -0.783300
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.682153, rho = -0.688288
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -5.026705, rho = -0.551618
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 63% (63/100) (classification)
Accuracy = 63.2% (632/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.672229, rho = -0.355024
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 90% (90/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.916926
obj = -8.530428, rho = -0.211912
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.832309
obj = -10.607726, rho = -0.165649
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.714502
obj = -12.947963, rho = -0.165069
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.608747
obj = -15.729225, rho = -0.147624
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.512671
obj = -19.095652, rho = -0.204068
nSV = 54, nBSV = 45
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.425843
obj = -23.296910, rho = -0.242423
nSV = 49, nBSV = 40
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.363405
obj = -28.578035, rho = -0.281061
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.307080
obj = -35.181832, rho = -0.283919
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.267579
obj = -43.327825, rho = -0.249363
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.234849
obj = -52.405817, rho = -0.166836
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.198089
obj = -62.274325, rho = -0.098731
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.169484
obj = -72.506093, rho = -0.047265
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.131875
obj = -83.579042, rho = -0.051326
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.952643, rho = 0.878817
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.353075, rho = 0.825684
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.910631, rho = 0.749255
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.674477, rho = 0.639316
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.694256, rho = 0.481174
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.997743, rho = 0.253694
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 71% (71/100) (classification)
Accuracy = 74.9% (749/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.534620, rho = -0.073523
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.885806
obj = -8.269824, rho = -0.096369
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.808440
obj = -10.267642, rho = -0.143165
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.701623
obj = -12.430874, rho = -0.088647
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.593220
obj = -14.978127, rho = -0.127278
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.500265
obj = -17.877722, rho = -0.165797
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.418235
obj = -21.014649, rho = -0.163109
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.343888
obj = -24.586392, rho = -0.164016
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.281030
obj = -28.397090, rho = -0.172307
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.228762
obj = -32.805915, rho = -0.147139
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.184914
obj = -37.695509, rho = -0.135358
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.144374
obj = -42.835403, rho = -0.121483
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.116733
obj = -49.000536, rho = -0.198771
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.097428
obj = -54.009433, rho = -0.313861
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.680000
obj = -0.671039, rho = 0.963068
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.680000
obj = -0.959604, rho = 0.946875
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.680000
obj = -1.368650, rho = 0.923555
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.680000
obj = -1.944542, rho = 0.889623
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.680000
obj = -2.747064, rho = 0.841229
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.680000
obj = -3.847928, rho = 0.771615
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.680000
obj = -5.320727, rho = 0.671471
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 66% (66/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.680000
obj = -7.210117, rho = 0.527428
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 77% (77/100) (classification)
Accuracy = 56.6% (566/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.680000
obj = -9.453780, rho = 0.320218
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 87% (87/100) (classification)
Accuracy = 85.2% (852/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.620000
obj = -12.030260, rho = 0.279884
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 92% (92/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.556950
obj = -15.077382, rho = 0.255216
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.487613
obj = -18.607630, rho = 0.270016
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.422523
obj = -22.700791, rho = 0.244330
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.359779
obj = -27.596030, rho = 0.186020
nSV = 41, nBSV = 32
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.309339
obj = -33.352393, rho = 0.168777
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.256355
obj = -39.983908, rho = 0.086196
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.218760
obj = -47.596421, rho = 0.314451
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.174331
obj = -56.613862, rho = 0.283533
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*.*
optimization finished, #iter = 148
nu = 0.148993
obj = -67.525891, rho = 0.477125
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.118994
obj = -80.662087, rho = 0.457924
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.915876, rho = 0.884817
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.302228, rho = 0.834315
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.841709, rho = 0.761671
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.584068, rho = 0.657176
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.582273, rho = 0.506865
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.874042, rho = 0.290650
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 66% (66/100) (classification)
Accuracy = 68.5% (685/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.434030, rho = -0.020365
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.876059
obj = -8.197062, rho = -0.118737
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.782778
obj = -10.251397, rho = -0.170662
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.679169
obj = -12.716691, rho = -0.170935
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.614235
obj = -15.625810, rho = -0.207711
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.516909
obj = -18.725100, rho = -0.204752
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.435851
obj = -22.281540, rho = -0.232381
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.363408
obj = -26.052921, rho = -0.306820
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.294225
obj = -30.483784, rho = -0.253821
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.243998
obj = -35.278193, rho = -0.286203
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.195124
obj = -40.260685, rho = -0.333342
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.155346
obj = -46.346670, rho = -0.332923
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.125868
obj = -52.983347, rho = -0.393949
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.101302
obj = -59.591854, rho = -0.466341
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -0.784100, rho = -0.965052
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -1.117862, rho = -0.949973
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -1.587241, rho = -0.928038
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -2.240235, rho = -0.896487
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -3.133636, rho = -0.851101
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -4.323777, rho = -0.785816
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -5.839229, rho = -0.691560
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 76% (76/100) (classification)
Accuracy = 66.1% (661/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -7.612524, rho = -0.556324
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 93% (93/100) (classification)
Accuracy = 89.6% (896/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.720000
obj = -9.637312, rho = -0.522944
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 96% (96/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.640000
obj = -12.035471, rho = -0.531049
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.562473
obj = -14.797505, rho = -0.464383
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.482710
obj = -18.030469, rho = -0.442436
nSV = 50, nBSV = 47
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.421712
obj = -21.759519, rho = -0.381967
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.348805
obj = -25.731185, rho = -0.420608
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.295011
obj = -30.021457, rho = -0.465200
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.234225
obj = -34.661621, rho = -0.455553
nSV = 30, nBSV = 19
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.189564
obj = -40.391380, rho = -0.462128
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.152102
obj = -46.903994, rho = -0.492929
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 210
nu = 0.126036
obj = -54.468366, rho = -0.509749
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.101168
obj = -62.961037, rho = -0.463013
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -0.840295, rho = 0.924726
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.196296, rho = 0.891970
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -1.695099, rho = 0.845191
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -2.385110, rho = 0.777315
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -3.320774, rho = 0.679679
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -4.548982, rho = 0.539918
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -6.072161, rho = 0.338200
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 81% (81/100) (classification)
Accuracy = 71.2% (712/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.859817
obj = -7.759272, rho = 0.047148
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.756241
obj = -9.510740, rho = -0.029320
nSV = 78, nBSV = 72
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.653201
obj = -11.522311, rho = -0.038583
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.547092
obj = -13.869372, rho = -0.017927
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.462174
obj = -16.653118, rho = 0.019464
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.383384
obj = -19.749240, rho = 0.040061
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.324293
obj = -23.315580, rho = 0.016367
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.271291
obj = -26.805516, rho = -0.089520
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 261
nu = 0.214483
obj = -30.519929, rho = -0.061102
nSV = 27, nBSV = 16
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.167278
obj = -35.151042, rho = -0.010143
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.134573
obj = -40.820996, rho = 0.049782
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.109956
obj = -46.861068, rho = 0.065576
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.087991
obj = -53.298004, rho = -0.007244
nSV = 11, nBSV = 6
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -0.842498, rho = 0.919197
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.200853, rho = 0.883769
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.704527, rho = 0.832808
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.404619, rho = 0.759502
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -3.361139, rho = 0.654056
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -4.632501, rho = 0.502377
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -6.244973, rho = 0.284194
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 79% (79/100) (classification)
Accuracy = 73.6% (736/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -8.125645, rho = 0.021728
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.797045
obj = -10.179829, rho = -0.040500
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.684808
obj = -12.424410, rho = -0.052487
nSV = 72, nBSV = 65
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.586727
obj = -15.114793, rho = -0.011309
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.491602
obj = -18.323236, rho = -0.045971
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.416399
obj = -22.333300, rho = -0.065304
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.352108
obj = -27.079318, rho = -0.154542
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.299280
obj = -32.745623, rho = -0.123275
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.252701
obj = -39.191514, rho = -0.104724
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.213833
obj = -46.552544, rho = -0.148782
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.178094
obj = -54.844507, rho = -0.198934
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.143651
obj = -64.515172, rho = -0.132672
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.119141
obj = -75.360348, rho = -0.103181
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.947158, rho = 0.840980
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.341726, rho = 0.771258
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.887146, rho = 0.670966
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.625885, rho = 0.526701
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.593712, rho = 0.319183
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 64% (64/100) (classification)
Accuracy = 58.4% (584/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.789704, rho = 0.020678
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 84% (84/100) (classification)
Accuracy = 83.8% (838/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.935518
obj = -6.164130, rho = -0.198718
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.838924
obj = -7.779646, rho = -0.146551
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.752421
obj = -9.679066, rho = -0.073848
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.657428
obj = -11.890725, rho = -0.076855
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.572344
obj = -14.378047, rho = -0.136948
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.477747
obj = -17.125916, rho = -0.176866
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.394755
obj = -20.494998, rho = -0.178477
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.327176
obj = -24.462406, rho = -0.159715
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.270362
obj = -29.285735, rho = -0.139774
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.220569
obj = -35.318357, rho = -0.162847
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.196964
obj = -42.542592, rho = -0.079886
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.157091
obj = -50.345990, rho = -0.054187
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.137744
obj = -59.384284, rho = -0.051665
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.109959
obj = -67.624619, rho = -0.079214
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.895638, rho = 0.900215
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.272966, rho = 0.856464
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.799307, rho = 0.793531
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.522432, rho = 0.703005
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.492283, rho = 0.572787
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.741844, rho = 0.385476
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 68% (68/100) (classification)
Accuracy = 62% (620/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.238176, rho = 0.116038
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 90% (90/100) (classification)
Accuracy = 88.4% (884/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.858386
obj = -7.863581, rho = -0.040710
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.767435
obj = -9.724638, rho = 0.024886
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.660641
obj = -11.852010, rho = -0.030512
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.568217
obj = -14.285742, rho = -0.019746
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.470061
obj = -17.048739, rho = 0.043461
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.394479
obj = -20.357842, rho = 0.009878
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.334628
obj = -24.019747, rho = 0.030433
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.271524
obj = -28.048230, rho = 0.109004
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.221227
obj = -32.420758, rho = 0.135059
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.179925
obj = -37.419734, rho = 0.122649
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.150980
obj = -42.341878, rho = 0.072094
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.117877
obj = -46.903229, rho = 0.093555
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*..*
optimization finished, #iter = 307
nu = 0.088908
obj = -51.575638, rho = 0.014245
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.916388, rho = -0.926615
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.303286, rho = -0.894439
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.843898, rho = -0.848156
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.588597, rho = -0.781580
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.591644, rho = -0.685814
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.893432, rho = -0.548059
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 68% (68/100) (classification)
Accuracy = 65.4% (654/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.474149, rho = -0.349905
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 93% (93/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.303050, rho = -0.281062
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -10.326171, rho = -0.228299
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.695042
obj = -12.602401, rho = -0.240972
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.592930
obj = -15.278209, rho = -0.181647
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.510725
obj = -18.440579, rho = -0.187423
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.424198
obj = -21.890646, rho = -0.204104
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.351393
obj = -26.175847, rho = -0.164925
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.296051
obj = -30.995112, rho = -0.200467
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.238692
obj = -36.717684, rho = -0.226767
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.199107
obj = -43.590854, rho = -0.314388
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.167699
obj = -51.306319, rho = -0.444448
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.134889
obj = -59.964593, rho = -0.512071
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.109931
obj = -70.628864, rho = -0.580625
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -0.821042, rho = -0.958492
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -1.169071, rho = -0.940292
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -1.656910, rho = -0.914113
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -2.332193, rho = -0.876456
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -3.248823, rho = -0.822289
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -4.454107, rho = -0.744371
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 61% (61/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -5.953533, rho = -0.632291
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 81% (81/100) (classification)
Accuracy = 71% (710/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.803853
obj = -7.661800, rho = -0.537822
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 91% (91/100) (classification)
Accuracy = 88.3% (883/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.732526
obj = -9.595552, rho = -0.444412
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 94% (94/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.659235
obj = -11.831860, rho = -0.384978
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.560968
obj = -14.310875, rho = -0.304857
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.471289
obj = -17.200597, rho = -0.298789
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.396354
obj = -20.625265, rho = -0.260377
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.325898
obj = -24.808514, rho = -0.249704
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.269624
obj = -30.011066, rho = -0.254177
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.230737
obj = -36.382923, rho = -0.209573
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.201964
obj = -43.807560, rho = -0.134970
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.165584
obj = -51.740858, rho = -0.093509
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.139584
obj = -60.198529, rho = -0.048724
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.115052
obj = -69.155791, rho = 0.024292
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.914510, rho = 0.882325
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.299400, rho = 0.830731
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.835858, rho = 0.756515
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.571962, rho = 0.649759
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.557224, rho = 0.496196
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.822214, rho = 0.275303
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 73% (73/100) (classification)
Accuracy = 67.3% (673/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.326789, rho = -0.042440
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 92.7% (927/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -7.944672, rho = -0.117997
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.780262
obj = -9.714634, rho = -0.064677
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.668139
obj = -11.732378, rho = -0.094309
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.560764
obj = -13.941088, rho = -0.052288
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.462352
obj = -16.535934, rho = -0.047014
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.386025
obj = -19.609166, rho = -0.033095
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.318009
obj = -23.125549, rho = -0.040838
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.260379
obj = -27.152211, rho = -0.063174
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.212748
obj = -32.193737, rho = -0.081011
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.176517
obj = -37.713593, rho = -0.176137
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.146581
obj = -43.765150, rho = -0.243590
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.119946
obj = -50.216874, rho = -0.254931
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.097592
obj = -55.776831, rho = -0.434726
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.895874, rho = -0.923954
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.273453, rho = -0.890611
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.800315, rho = -0.842650
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.524517, rho = -0.773660
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.496597, rho = -0.674421
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.750771, rho = -0.531670
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 61% (61/100) (classification)
Accuracy = 60.6% (606/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -6.256646, rho = -0.326346
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 93% (93/100) (classification)
Accuracy = 88% (880/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.858282
obj = -7.955752, rho = -0.211855
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.774092
obj = -9.837870, rho = -0.109547
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.668817
obj = -12.029410, rho = -0.109260
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.562591
obj = -14.535167, rho = -0.166785
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.480389
obj = -17.523791, rho = -0.193471
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.401232
obj = -21.004326, rho = -0.221968
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.334001
obj = -25.128129, rho = -0.192623
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.281274
obj = -29.848289, rho = -0.296494
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.235163
obj = -35.509710, rho = -0.315432
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.188126
obj = -42.184707, rho = -0.367074
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.164632
obj = -50.390801, rho = -0.483329
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.131138
obj = -58.716457, rho = -0.506199
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.113534
obj = -67.804767, rho = -0.243078
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.931975, rho = -0.912690
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.322925, rho = -0.874409
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.866390, rho = -0.819344
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.609036, rho = -0.740135
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.596393, rho = -0.626197
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.849255, rho = -0.462304
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 77% (77/100) (classification)
Accuracy = 74.7% (747/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.953305
obj = -6.305573, rho = -0.239562
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.869391
obj = -7.934481, rho = -0.201071
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.759043
obj = -9.802682, rho = -0.188541
nSV = 80, nBSV = 74
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.662896
obj = -12.042956, rho = -0.150803
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.558543
obj = -14.704367, rho = -0.146121
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.485510
obj = -17.871841, rho = -0.208760
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.402868
obj = -21.566708, rho = -0.230761
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.338715
obj = -26.195881, rho = -0.233412
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.282852
obj = -32.056828, rho = -0.182571
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.240930
obj = -39.745919, rho = -0.174212
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.211152
obj = -48.735662, rho = -0.220975
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.178191
obj = -59.711999, rho = -0.104394
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 169
nu = 0.147479
obj = -73.597568, rho = -0.074481
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.130415
obj = -91.474484, rho = -0.012305
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.933505, rho = -0.917025
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.326090, rho = -0.880645
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.872939, rho = -0.828314
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.622588, rho = -0.753038
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.624434, rho = -0.644757
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.907275, rho = -0.489001
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 76% (76/100) (classification)
Accuracy = 77.6% (776/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.434086, rho = -0.320630
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.888477
obj = -8.171806, rho = -0.239089
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.795529
obj = -10.153967, rho = -0.208736
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.678303
obj = -12.467719, rho = -0.176494
nSV = 71, nBSV = 64
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.589166
obj = -15.342862, rho = -0.136682
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.512685
obj = -18.580173, rho = -0.021450
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.426446
obj = -22.236566, rho = -0.084063
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.352977
obj = -26.608112, rho = -0.138272
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.291981
obj = -32.022598, rho = -0.123602
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.246768
obj = -38.810684, rho = -0.190132
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.207739
obj = -47.114990, rho = -0.350814
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 184
nu = 0.174010
obj = -56.616101, rho = -0.454938
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.153752
obj = -66.582357, rho = -0.580703
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.124912
obj = -75.778294, rho = -0.550771
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -0.860544, rho = 0.905474
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.225579, rho = 0.864029
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.737545, rho = 0.804413
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -2.446837, rho = 0.718658
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.410953, rho = 0.595303
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.681568, rho = 0.417864
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 59% (59/100) (classification)
Accuracy = 56.4% (564/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.268819, rho = 0.162626
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 84% (84/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.847467
obj = -8.076371, rho = -0.076895
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.770420
obj = -10.177303, rho = -0.068996
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.676607
obj = -12.692524, rho = -0.036303
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.592971
obj = -15.622064, rho = -0.004494
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.511481
obj = -19.106005, rho = -0.073076
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.432110
obj = -23.196351, rho = -0.030250
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.371408
obj = -27.982630, rho = 0.062215
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.309562
obj = -33.585753, rho = 0.098162
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.264220
obj = -39.665218, rho = 0.183028
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.212061
obj = -46.608719, rho = 0.197201
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.173576
obj = -55.294733, rho = 0.218379
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.144340
obj = -66.300566, rho = 0.387306
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.119503
obj = -79.908976, rho = 0.413795
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.880604, rho = 0.927319
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.254479, rho = 0.895700
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.779199, rho = 0.849970
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.506926, rho = 0.784189
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.497741, rho = 0.689566
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.807141, rho = 0.553457
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 56% (56/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.900000
obj = -6.450980, rho = 0.358046
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 72% (72/100) (classification)
Accuracy = 71.8% (718/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -8.339203, rho = 0.153634
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.816638
obj = -10.382754, rho = 0.012761
nSV = 85, nBSV = 80
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.707955
obj = -12.554405, rho = -0.008080
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.598274
obj = -15.034900, rho = -0.027910
nSV = 64, nBSV = 57
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.500812
obj = -17.940807, rho = -0.048103
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.410125
obj = -21.371523, rho = -0.098450
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.346591
obj = -25.489015, rho = 0.002152
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.291986
obj = -29.924223, rho = 0.044634
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.231273
obj = -35.022423, rho = 0.052774
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.186530
obj = -41.587146, rho = 0.071833
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.152334
obj = -49.932894, rho = 0.100493
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.128741
obj = -60.365282, rho = 0.096253
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.113828
obj = -72.080756, rho = 0.085804
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.875914, rho = 0.889960
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.244768, rho = 0.841713
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.759104, rho = 0.772312
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.465347, rho = 0.672482
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -3.411708, rho = 0.528882
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -4.629128, rho = 0.322320
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 69% (69/100) (classification)
Accuracy = 64.6% (646/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.082630, rho = 0.025156
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -7.696427, rho = -0.112075
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.755000
obj = -9.502435, rho = -0.000016
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.642139
obj = -11.534031, rho = -0.008746
nSV = 69, nBSV = 62
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.556573
obj = -13.799479, rho = 0.054197
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.472001
obj = -16.192542, rho = -0.016674
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.383826
obj = -18.833162, rho = -0.022509
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.310580
obj = -21.657436, rho = 0.016985
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.246344
obj = -24.989085, rho = 0.056890
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.199798
obj = -28.752224, rho = 0.046296
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.163318
obj = -32.810611, rho = 0.044515
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*...*
optimization finished, #iter = 306
nu = 0.126082
obj = -37.041148, rho = 0.045830
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.101301
obj = -42.322700, rho = 0.038439
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.082598
obj = -47.097032, rho = 0.103770
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -0.838012, rho = 0.894332
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.191570, rho = 0.848001
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.685319, rho = 0.781357
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -2.364875, rho = 0.685494
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -3.278904, rho = 0.547598
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -4.462350, rho = 0.348325
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 63% (63/100) (classification)
Accuracy = 58.2% (582/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -5.892915, rho = 0.063278
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 90% (90/100) (classification)
Accuracy = 82.7% (827/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.817300
obj = -7.450186, rho = -0.128941
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.730470
obj = -9.178950, rho = -0.210669
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.628589
obj = -11.120233, rho = -0.271903
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.536182
obj = -13.268568, rho = -0.275214
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.446004
obj = -15.714859, rho = -0.288592
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.379917
obj = -18.432934, rho = -0.302702
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.315115
obj = -20.972501, rho = -0.301020
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.241524
obj = -23.461346, rho = -0.324585
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.188851
obj = -26.569991, rho = -0.330937
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.149816
obj = -29.885872, rho = -0.388601
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.120684
obj = -33.172757, rho = -0.352031
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.092132
obj = -35.694973, rho = -0.319748
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.068173
obj = -38.640375, rho = -0.314649
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949072, rho = -0.903387
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.345687, rho = -0.861027
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.895343, rho = -0.800094
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.642845, rho = -0.712446
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.628804, rho = -0.586368
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 55% (55/100) (classification)
Accuracy = 53.9% (539/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.862315, rho = -0.405011
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 83.3% (833/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.959546
obj = -6.275694, rho = -0.197353
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.856256
obj = -7.897628, rho = -0.236660
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.777093
obj = -9.755431, rho = -0.176790
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.661788
obj = -11.825359, rho = -0.107086
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.563873
obj = -14.192147, rho = -0.125845
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.465177
obj = -17.042327, rho = -0.132154
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.395724
obj = -20.408305, rho = -0.147862
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.324755
obj = -24.365485, rho = -0.154531
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 261
nu = 0.265213
obj = -29.370881, rho = -0.073563
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 295
nu = 0.224140
obj = -35.811733, rho = -0.018205
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.184580
obj = -43.979578, rho = -0.040489
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.158357
obj = -54.696108, rho = 0.010384
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.142608
obj = -67.250702, rho = 0.143138
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.124803
obj = -79.908585, rho = 0.140106
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -0.948820, rho = 0.836814
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.345166, rho = 0.765265
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.894264, rho = 0.662345
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -2.640613, rho = 0.514300
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -3.624186, rho = 0.301345
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 64% (64/100) (classification)
Accuracy = 60.5% (605/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.852760, rho = -0.004980
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 92% (92/100) (classification)
Accuracy = 92.5% (925/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.922084
obj = -6.289286, rho = -0.163330
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.857419
obj = -8.053002, rho = -0.143545
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.765721
obj = -10.169685, rho = -0.092379
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.679085
obj = -12.681719, rho = -0.090064
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.583046
obj = -15.746852, rho = -0.071586
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.500122
obj = -19.551351, rho = -0.044769
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.438410
obj = -24.258333, rho = -0.110830
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.374145
obj = -29.984611, rho = -0.146368
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.331235
obj = -36.513512, rho = -0.130267
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.272719
obj = -44.483488, rho = -0.069663
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.228605
obj = -55.002150, rho = -0.158062
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.194859
obj = -69.087322, rho = -0.216579
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.172856
obj = -86.845021, rho = -0.376085
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 115
nu = 0.151192
obj = -108.354176, rho = -0.560117
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949704, rho = -0.904034
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.346994, rho = -0.861957
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.898048, rho = -0.801432
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.648441, rho = -0.714370
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.640384, rho = -0.589136
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 53% (53/100) (classification)
Accuracy = 53.8% (538/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.886276, rho = -0.408993
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 84.7% (847/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.966432
obj = -6.306008, rho = -0.201976
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 95% (95/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.869703
obj = -7.911014, rho = -0.183917
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -9.797212, rho = -0.161671
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.661889
obj = -11.989721, rho = -0.097791
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.570867
obj = -14.541455, rho = -0.103619
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.489081
obj = -17.448009, rho = -0.045823
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.411509
obj = -20.606013, rho = -0.000558
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.329319
obj = -24.171408, rho = 0.003577
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.273400
obj = -28.619623, rho = -0.034791
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.223511
obj = -33.838687, rho = -0.032460
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*...*
optimization finished, #iter = 311
nu = 0.181963
obj = -40.010106, rho = -0.035949
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.154084
obj = -46.941800, rho = 0.003779
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.125157
obj = -54.707588, rho = -0.060855
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*....*
optimization finished, #iter = 536
nu = 0.103256
obj = -62.154525, rho = -0.200926
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.914475, rho = 0.897920
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.299329, rho = 0.853164
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.835710, rho = 0.788783
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.571655, rho = 0.696175
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.556588, rho = 0.562963
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.820897, rho = 0.371344
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 69% (69/100) (classification)
Accuracy = 68.7% (687/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.324064, rho = 0.095711
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 93% (93/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.863041
obj = -7.951746, rho = -0.002747
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.771097
obj = -9.837501, rho = -0.099677
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.660000
obj = -12.063405, rho = -0.084626
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.560220
obj = -14.767290, rho = -0.129963
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.484644
obj = -18.002823, rho = -0.155684
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.408342
obj = -21.903568, rho = -0.122616
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.345962
obj = -26.646215, rho = -0.134095
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.300407
obj = -31.816088, rho = -0.123065
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.247914
obj = -37.683968, rho = -0.190852
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.202876
obj = -44.853180, rho = -0.139188
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.164731
obj = -53.878374, rho = -0.122779
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.137933
obj = -65.592604, rho = -0.063313
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.118988
obj = -79.243198, rho = -0.082191
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -0.805347, rho = -0.960626
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -1.149211, rho = -0.943362
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -1.633961, rho = -0.918529
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -2.310806, rho = -0.882808
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -3.242114, rho = -0.831426
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -4.494229, rho = -0.757514
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -6.114232, rho = -0.651197
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 65% (65/100) (classification)
Accuracy = 59.9% (599/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -8.069804, rho = -0.498264
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 87% (87/100) (classification)
Accuracy = 88.2% (882/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.796147
obj = -10.201728, rho = -0.332247
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.682443
obj = -12.489768, rho = -0.278796
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.596310
obj = -15.242607, rho = -0.215335
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.512358
obj = -18.186652, rho = -0.220223
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.421767
obj = -21.709480, rho = -0.172049
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.343558
obj = -25.850525, rho = -0.153849
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.287138
obj = -30.915260, rho = -0.087704
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.241555
obj = -36.775815, rho = -0.138756
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.196199
obj = -43.843543, rho = -0.108490
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.160861
obj = -52.822905, rho = -0.102722
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.131658
obj = -64.573799, rho = -0.074098
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.111832
obj = -80.235280, rho = -0.047284
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.896648, rho = -0.927295
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.275054, rho = -0.895417
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.803627, rho = -0.849562
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.531371, rho = -0.783603
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.510779, rho = -0.688724
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.780116, rho = -0.552245
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 65% (65/100) (classification)
Accuracy = 59.7% (597/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.317364, rho = -0.355927
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 88% (88/100) (classification)
Accuracy = 89.3% (893/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.878542
obj = -7.998198, rho = -0.158104
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.772082
obj = -9.891395, rho = -0.128227
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.664410
obj = -12.078138, rho = -0.115328
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.564507
obj = -14.680281, rho = -0.127487
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.483355
obj = -17.860317, rho = -0.172075
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.411231
obj = -21.566656, rho = -0.162515
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.347502
obj = -25.757724, rho = -0.168565
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.294192
obj = -30.709038, rho = -0.182600
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 203
nu = 0.242669
obj = -35.865966, rho = -0.256099
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.193274
obj = -42.076684, rho = -0.293389
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 301
nu = 0.158335
obj = -49.361864, rho = -0.340231
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.137072
obj = -56.919854, rho = -0.543319
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.106267
obj = -64.735439, rho = -0.566108
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.949359, rho = 0.829402
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.346281, rho = 0.754603
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.896572, rho = 0.647009
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.645387, rho = 0.492240
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.634065, rho = 0.269613
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 60% (60/100) (classification)
Accuracy = 63.6% (636/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.873200, rho = -0.050625
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 87.7% (877/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.944059
obj = -6.305993, rho = -0.313432
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 92% (92/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.859548
obj = -7.990781, rho = -0.282032
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 93% (93/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.758496
obj = -10.018708, rho = -0.295140
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 94% (94/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.661637
obj = -12.490976, rho = -0.271507
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.578340
obj = -15.481097, rho = -0.189277
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 95% (95/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.493747
obj = -19.109099, rho = -0.151008
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.422624
obj = -23.657775, rho = -0.090154
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.359326
obj = -29.371910, rho = -0.124767
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.318100
obj = -36.667547, rho = -0.230642
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.272693
obj = -45.643610, rho = -0.250121
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.235976
obj = -57.065536, rho = -0.253510
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.199549
obj = -72.088680, rho = -0.321355
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.175904
obj = -91.869643, rho = -0.326091
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.166413
obj = -115.462617, rho = -0.255817
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.860600, rho = -0.930932
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.225694, rho = -0.900649
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.737782, rho = -0.857088
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -2.447328, rho = -0.794428
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.411967, rho = -0.704296
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.683667, rho = -0.574644
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 59% (59/100) (classification)
Accuracy = 54.2% (542/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.273162, rho = -0.388147
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 87% (87/100) (classification)
Accuracy = 85.1% (851/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.097530, rho = -0.207875
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.761774
obj = -10.194760, rho = -0.174971
nSV = 79, nBSV = 73
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.680446
obj = -12.748393, rho = -0.178658
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.596521
obj = -15.654748, rho = -0.119816
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.509095
obj = -19.164420, rho = -0.057748
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.437310
obj = -23.374042, rho = -0.120788
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.369660
obj = -28.427603, rho = -0.141480
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.314002
obj = -34.578163, rho = -0.191632
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.262286
obj = -41.907197, rho = -0.240274
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.218412
obj = -50.940312, rho = -0.217121
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.186577
obj = -62.262259, rho = -0.257087
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.161359
obj = -76.150088, rho = -0.387688
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.132413
obj = -92.684866, rho = -0.254767
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -0.875894, rho = 0.909209
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.244727, rho = 0.869380
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.759020, rho = 0.812110
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.465172, rho = 0.729730
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.411346, rho = 0.611230
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.628380, rho = 0.440774
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 64% (64/100) (classification)
Accuracy = 57.5% (575/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.081083, rho = 0.195582
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 92% (92/100) (classification)
Accuracy = 87% (870/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.846860
obj = -7.624123, rho = 0.041665
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -9.366307, rho = 0.016312
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.650515
obj = -11.300797, rho = -0.116276
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.545478
obj = -13.342923, rho = -0.101375
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.453322
obj = -15.657727, rho = -0.088198
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.365380
obj = -18.164433, rho = -0.103056
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.292172
obj = -21.292029, rho = -0.058215
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.247552
obj = -24.784090, rho = 0.020423
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 281
nu = 0.194212
obj = -28.682426, rho = 0.062817
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.152423
obj = -33.779828, rho = 0.049482
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.126302
obj = -40.177732, rho = 0.122463
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 188
nu = 0.106623
obj = -47.136122, rho = 0.251665
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.084365
obj = -55.465267, rho = 0.298955
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.915591, rho = 0.871263
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.301636, rho = 0.814819
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.840485, rho = 0.733626
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.581535, rho = 0.616834
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -3.577033, rho = 0.449437
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -4.863201, rho = 0.208043
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 73% (73/100) (classification)
Accuracy = 72.5% (725/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.938184
obj = -6.411645, rho = -0.131864
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.234403, rho = -0.098480
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.775251
obj = -10.443681, rho = -0.091090
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.689027
obj = -13.130675, rho = -0.103524
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.616493
obj = -16.302851, rho = -0.090450
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.533355
obj = -20.002230, rho = -0.042416
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.451152
obj = -24.425295, rho = -0.079453
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.394879
obj = -29.402000, rho = 0.072953
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.322877
obj = -35.276991, rho = 0.038922
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.276091
obj = -42.465514, rho = 0.093292
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.228489
obj = -50.819375, rho = 0.026601
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.192649
obj = -59.884330, rho = -0.026246
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.163915
obj = -69.713464, rho = -0.064018
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.131933
obj = -78.893869, rho = -0.029682
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -0.840946, rho = -0.941709
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.197642, rho = -0.916151
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.697882, rho = -0.879387
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.390869, rho = -0.826505
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.332690, rho = -0.750436
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.573636, rho = -0.641014
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 53.6% (536/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -6.123174, rho = -0.483617
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 84% (84/100) (classification)
Accuracy = 76% (760/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.895557, rho = -0.322679
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -9.831280, rho = -0.296212
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 94% (94/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.655673
obj = -12.119500, rho = -0.258736
nSV = 69, nBSV = 62
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.574618
obj = -14.807245, rho = -0.229853
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.487983
obj = -17.956655, rho = -0.241363
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.406179
obj = -21.779356, rho = -0.224586
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.342598
obj = -26.357674, rho = -0.166096
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.286992
obj = -32.183476, rho = -0.176444
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.246742
obj = -39.552798, rho = -0.265785
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.207855
obj = -47.994187, rho = -0.311044
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.172360
obj = -59.113370, rho = -0.317900
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.152786
obj = -72.475437, rho = -0.259581
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.131931
obj = -87.025314, rho = -0.178687
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.918259, rho = 0.914504
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.307158, rho = 0.877018
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.851911, rho = 0.823097
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.605176, rho = 0.745060
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -3.625949, rho = 0.633963
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -4.964415, rho = 0.473475
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 56% (56/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -6.621029, rho = 0.242268
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 88% (88/100) (classification)
Accuracy = 81.4% (814/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.916465
obj = -8.460471, rho = -0.011802
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.818429
obj = -10.514015, rho = -0.024854
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.693833
obj = -12.969758, rho = -0.034537
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.611093
obj = -15.845214, rho = -0.106260
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.516116
obj = -19.279225, rho = -0.071713
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.447000
obj = -23.288570, rho = -0.074671
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.366725
obj = -28.003542, rho = -0.123954
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.304769
obj = -33.949723, rho = -0.147140
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.251985
obj = -41.755572, rho = -0.138091
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.219713
obj = -51.281981, rho = -0.063478
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.182189
obj = -63.546438, rho = -0.008800
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.157552
obj = -79.140832, rho = 0.010629
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 210
nu = 0.135865
obj = -99.843961, rho = 0.146179
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.914766, rho = 0.883713
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 47.4% (474/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.299930, rho = 0.832727
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 47.4% (474/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.836955, rho = 0.759386
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 47.4% (474/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.574231, rho = 0.653889
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 47.4% (474/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.561918, rho = 0.502137
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 47.7% (477/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.831926, rho = 0.283849
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 69% (69/100) (classification)
Accuracy = 64.6% (646/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.346884, rho = -0.030147
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 92% (92/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.879348
obj = -8.026588, rho = -0.054598
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -9.961961, rho = -0.110887
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.673478
obj = -12.158664, rho = -0.080574
nSV = 70, nBSV = 63
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.576640
obj = -14.839844, rho = -0.174555
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.489250
obj = -17.819116, rho = -0.219998
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.407430
obj = -21.488353, rho = -0.186776
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.346912
obj = -25.585339, rho = -0.129084
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.283584
obj = -30.403486, rho = -0.184114
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.234014
obj = -36.215832, rho = -0.182130
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.198100
obj = -43.205420, rho = -0.236221
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*..*..*
optimization finished, #iter = 369
nu = 0.164028
obj = -51.111744, rho = -0.162264
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.133770
obj = -60.264039, rho = -0.130936
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.115312
obj = -70.706093, rho = -0.179430
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.951066, rho = -0.902872
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.349813, rho = -0.860286
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.903881, rho = -0.799029
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.660511, rho = -0.710913
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.665357, rho = -0.584163
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 54% (54/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.937948, rho = -0.401839
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 82% (82/100) (classification)
Accuracy = 82% (820/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.410895, rho = -0.328978
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 94% (94/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.889046
obj = -8.040853, rho = -0.226471
nSV = 92, nBSV = 87
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.779487
obj = -9.833391, rho = -0.143079
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.658806
obj = -12.019210, rho = -0.129485
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.562634
obj = -14.703284, rho = -0.081208
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.482917
obj = -17.914509, rho = -0.054162
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.410642
obj = -21.608749, rho = -0.007846
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.341957
obj = -25.949601, rho = 0.085529
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.288304
obj = -31.218326, rho = 0.098867
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.235439
obj = -37.696576, rho = 0.112434
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.197718
obj = -46.104653, rho = 0.077779
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.169531
obj = -55.769281, rho = 0.120305
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.143469
obj = -67.958141, rho = 0.128020
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.125222
obj = -80.605994, rho = 0.126015
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.877898, rho = -0.931438
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.248872, rho = -0.901376
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.767599, rho = -0.858780
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.482924, rho = -0.796861
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.448077, rho = -0.707795
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.704380, rho = -0.579678
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 64% (64/100) (classification)
Accuracy = 56.2% (562/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.238339, rho = -0.395388
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 91% (91/100) (classification)
Accuracy = 87.6% (876/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.934503, rho = -0.262652
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.752681
obj = -9.910863, rho = -0.280509
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.666759
obj = -12.252785, rho = -0.222912
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.576161
obj = -15.020258, rho = -0.199820
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.498061
obj = -18.208451, rho = -0.210506
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.423905
obj = -21.799435, rho = -0.181367
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.345630
obj = -26.080504, rho = -0.162675
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.296022
obj = -30.932827, rho = -0.117684
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*..*
optimization finished, #iter = 207
nu = 0.240197
obj = -36.238505, rho = -0.027425
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.197440
obj = -42.941246, rho = -0.037739
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.159381
obj = -51.068615, rho = -0.010428
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.131167
obj = -61.451770, rho = -0.033652
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.112052
obj = -73.816056, rho = -0.062410
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -0.938070, rho = 0.918483
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -1.335536, rho = 0.882743
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -1.892483, rho = 0.831331
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -2.663027, rho = 0.757378
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -3.708106, rho = 0.651001
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -5.080407, rho = 0.498249
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 55% (55/100) (classification)
Accuracy = 56.7% (567/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -6.783345, rho = 0.278256
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 89.4% (894/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -8.682060, rho = 0.025503
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.826213
obj = -10.799054, rho = 0.030921
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.732144
obj = -13.269476, rho = 0.014552
nSV = 75, nBSV = 69
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.631558
obj = -16.123982, rho = -0.060277
nSV = 64, nBSV = 62
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.528430
obj = -19.429115, rho = -0.098625
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.445872
obj = -23.486525, rho = -0.028115
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.376069
obj = -28.112589, rho = -0.048949
nSV = 43, nBSV = 34
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.307253
obj = -33.781675, rho = -0.054949
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.258145
obj = -40.832015, rho = -0.123533
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.217753
obj = -49.393624, rho = -0.173246
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.177327
obj = -60.189680, rho = -0.194811
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.150211
obj = -74.171325, rho = -0.269960
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
...*..*
optimization finished, #iter = 510
nu = 0.131049
obj = -91.372344, rho = -0.164145
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.933787, rho = 0.891676
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.326674, rho = 0.844181
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.874146, rho = 0.775863
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.625085, rho = 0.677590
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -3.629619, rho = 0.533954
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -4.918004, rho = 0.329616
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 67% (67/100) (classification)
Accuracy = 67.3% (673/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -6.447311, rho = 0.035686
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.886555
obj = -8.122358, rho = -0.025461
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.788062
obj = -10.000263, rho = -0.043025
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.686559
obj = -12.140998, rho = -0.047869
nSV = 71, nBSV = 64
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.585246
obj = -14.597960, rho = -0.013012
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.498499
obj = -17.125921, rho = -0.063513
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.403126
obj = -19.862868, rho = -0.002381
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.327871
obj = -22.891748, rho = -0.050580
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.258738
obj = -26.555811, rho = -0.054253
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.208276
obj = -30.879113, rho = -0.074107
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.170291
obj = -36.019981, rho = -0.025855
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.139226
obj = -41.755194, rho = -0.024326
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.112979
obj = -47.795120, rho = 0.024115
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.088819
obj = -54.653947, rho = -0.053449
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.966943, rho = -0.008619
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.370050, rho = -0.012399
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.927609, rho = -0.017835
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.683508, rho = -0.025654
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.675398, rho = -0.036902
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.904720, rho = -0.053082
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.961889
obj = -6.288450, rho = -0.082323
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.878733
obj = -7.831081, rho = -0.032553
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.767530
obj = -9.511000, rho = 0.003210
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.654329
obj = -11.385506, rho = 0.015902
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.544675
obj = -13.519801, rho = 0.016503
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.457973
obj = -16.057087, rho = -0.008126
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.371537
obj = -18.969836, rho = 0.084099
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.308109
obj = -22.246808, rho = 0.023364
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.249980
obj = -26.099430, rho = -0.032275
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.199987
obj = -30.746979, rho = -0.062620
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.172175
obj = -36.214923, rho = -0.010115
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.140229
obj = -41.275692, rho = -0.136752
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.110860
obj = -47.157111, rho = -0.150334
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.090975
obj = -53.583632, rho = -0.074178
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.901800, rho = -0.937185
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 47.5% (475/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.285716, rho = -0.909644
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 47.5% (475/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.825688, rho = -0.870027
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 47.5% (475/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.577018, rho = -0.813041
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 47.5% (475/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.605228, rho = -0.731068
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 47.5% (475/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.975543, rho = -0.613155
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.721731, rho = -0.443543
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 79% (79/100) (classification)
Accuracy = 78.9% (789/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -8.768097, rho = -0.212176
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.842730
obj = -11.060801, rho = -0.181950
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.738926
obj = -13.747483, rho = -0.114206
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.640409
obj = -16.929454, rho = -0.106444
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.546696
obj = -20.771959, rho = -0.064577
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.475105
obj = -25.233594, rho = -0.118125
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.400508
obj = -30.479307, rho = -0.131774
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.338337
obj = -36.501076, rho = -0.144059
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.286127
obj = -43.226129, rho = -0.087523
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.236558
obj = -50.668188, rho = -0.156102
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.199156
obj = -58.739045, rho = 0.056725
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 225
nu = 0.163065
obj = -65.738133, rho = 0.014469
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.127858
obj = -71.848488, rho = -0.080366
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -0.875792, rho = 0.899771
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.244516, rho = 0.855826
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.758584, rho = 0.792613
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.464269, rho = 0.701684
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.409479, rho = 0.570887
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.624515, rho = 0.382743
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 69% (69/100) (classification)
Accuracy = 63.2% (632/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.073086, rho = 0.112106
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 93% (93/100) (classification)
Accuracy = 91.7% (917/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -7.616718, rho = 0.001438
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.731627
obj = -9.430036, rho = -0.007619
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.640000
obj = -11.617880, rho = 0.038925
nSV = 65, nBSV = 63
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.553139
obj = -14.009787, rho = 0.060076
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.465301
obj = -16.773205, rho = -0.065596
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.380130
obj = -20.119925, rho = -0.086903
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.321777
obj = -24.092493, rho = -0.094378
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.264357
obj = -29.051707, rho = -0.139860
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.217636
obj = -35.493069, rho = -0.187456
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.190199
obj = -43.575144, rho = -0.345312
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.159440
obj = -53.202547, rho = -0.431416
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.134383
obj = -65.293553, rho = -0.538879
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.116158
obj = -80.702748, rho = -0.580626
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.970884, rho = -0.024016
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.378205, rho = -0.034545
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.944483, rho = -0.049691
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.718424, rho = -0.071479
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.747644, rho = -0.102818
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.054208, rho = -0.147899
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.590033, rho = -0.140144
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.914286
obj = -8.287152, rho = -0.164141
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.805901
obj = -10.219495, rho = -0.117392
nSV = 83, nBSV = 78
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.709133
obj = -12.381627, rho = -0.100737
nSV = 73, nBSV = 65
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.593122
obj = -14.830167, rho = -0.160146
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.495362
obj = -17.539335, rho = -0.199313
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.413520
obj = -20.681432, rho = -0.246421
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.336787
obj = -24.229956, rho = -0.206328
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.270543
obj = -28.333803, rho = -0.257324
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.219708
obj = -33.218632, rho = -0.208728
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.182039
obj = -39.022138, rho = -0.156428
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.150582
obj = -45.493555, rho = -0.171619
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.120733
obj = -52.495583, rho = -0.240979
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.096742
obj = -60.894824, rho = -0.255474
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.932849, rho = 0.863759
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.324732, rho = 0.804024
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.870130, rho = 0.718099
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.616775, rho = 0.594499
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.612404, rho = 0.416708
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 54% (54/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.882385, rho = 0.160963
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 74% (74/100) (classification)
Accuracy = 76.9% (769/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.931390
obj = -6.385705, rho = -0.077333
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.873658
obj = -8.136796, rho = -0.288791
nSV = 90, nBSV = 85
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.770013
obj = -10.219258, rho = -0.213418
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 94% (94/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.679113
obj = -12.736019, rho = -0.219488
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 95% (95/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.585456
obj = -15.814077, rho = -0.296053
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.503547
obj = -19.709043, rho = -0.284942
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.441886
obj = -24.390275, rho = -0.286226
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.385692
obj = -29.908410, rho = -0.224235
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 170
nu = 0.322669
obj = -36.485792, rho = -0.187975
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.270110
obj = -45.041325, rho = -0.175213
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.229000
obj = -56.327368, rho = -0.198556
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.198893
obj = -71.059783, rho = -0.219223
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.176309
obj = -89.949694, rho = -0.396951
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.154207
obj = -113.550764, rho = -0.394295
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.933760, rho = -0.915199
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.326618, rho = -0.878019
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.874030, rho = -0.824536
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.624846, rho = -0.747604
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.629105, rho = -0.636941
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 56% (56/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.916942, rho = -0.477757
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 81% (81/100) (classification)
Accuracy = 77.8% (778/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.453185, rho = -0.299709
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.295980, rho = -0.267341
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.789452
obj = -10.384992, rho = -0.286867
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.700000
obj = -12.929081, rho = -0.210845
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.617723
obj = -15.702394, rho = -0.187013
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.510926
obj = -18.960004, rho = -0.195268
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.434914
obj = -23.020250, rho = -0.251328
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.366395
obj = -27.691345, rho = -0.302844
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.304154
obj = -33.405270, rho = -0.258403
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.262386
obj = -39.669546, rho = -0.208721
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.215051
obj = -46.762438, rho = -0.230380
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.182553
obj = -54.135498, rho = -0.212138
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*...*
optimization finished, #iter = 300
nu = 0.149875
obj = -60.595168, rho = -0.188704
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*..*
optimization finished, #iter = 362
nu = 0.114727
obj = -67.305472, rho = -0.142853
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.934443, rho = 0.864157
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.328031, rho = 0.804597
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.876954, rho = 0.718923
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.630895, rho = 0.595684
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.641621, rho = 0.418412
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.942839, rho = 0.163415
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 77% (77/100) (classification)
Accuracy = 78.5% (785/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.498697, rho = -0.203386
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.877426
obj = -8.300302, rho = -0.180770
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.799723
obj = -10.423883, rho = -0.148009
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.693870
obj = -12.964569, rho = -0.135039
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.612150
obj = -15.976595, rho = -0.066111
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.523338
obj = -19.467631, rho = -0.049664
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.440681
obj = -23.570260, rho = -0.010886
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.367527
obj = -28.564212, rho = 0.066207
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.305213
obj = -35.178895, rho = 0.051945
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.260824
obj = -43.717551, rho = 0.044833
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.227900
obj = -54.431573, rho = 0.108275
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.195003
obj = -67.762635, rho = 0.172771
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.173982
obj = -83.518385, rho = 0.224585
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.150126
obj = -101.119553, rho = 0.159259
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -0.857408, rho = 0.902451
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.219090, rho = 0.859681
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.724120, rho = 0.798128
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -2.419056, rho = 0.709620
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -3.353474, rho = 0.582406
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -4.562638, rho = 0.399223
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 65% (65/100) (classification)
Accuracy = 57.1% (571/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -6.022736, rho = 0.135813
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 89% (89/100) (classification)
Accuracy = 84.4% (844/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -7.667287, rho = -0.002930
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.734741
obj = -9.568455, rho = -0.070758
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.642330
obj = -11.784094, rho = -0.026456
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.557275
obj = -14.431664, rho = -0.001124
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.472393
obj = -17.406938, rho = 0.043984
nSV = 51, nBSV = 42
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.399524
obj = -21.023230, rho = 0.139149
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.331730
obj = -25.376787, rho = 0.178021
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.274904
obj = -30.771670, rho = 0.192217
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.236134
obj = -37.448062, rho = 0.164415
nSV = 25, nBSV = 21
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.198669
obj = -45.574053, rho = 0.104732
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.166150
obj = -55.691534, rho = 0.135431
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.140587
obj = -68.259105, rho = 0.207209
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.117945
obj = -84.809347, rho = 0.202768
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.896093, rho = 0.912426
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.273906, rho = 0.874029
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.801251, rho = 0.818796
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.526455, rho = 0.739348
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.500606, rho = 0.625065
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.759067, rho = 0.460674
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 69% (69/100) (classification)
Accuracy = 60% (600/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.273811, rho = 0.224207
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 93% (93/100) (classification)
Accuracy = 87.1% (871/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.947152, rho = 0.095145
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.768728
obj = -9.852154, rho = 0.103308
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.669561
obj = -12.046049, rho = 0.158620
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.583788
obj = -14.418582, rho = 0.172755
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.483147
obj = -16.911448, rho = 0.136361
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.391716
obj = -19.805395, rho = 0.160664
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.323129
obj = -23.143421, rho = 0.156475
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.262778
obj = -27.020745, rho = 0.223822
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.212946
obj = -31.552165, rho = 0.218185
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.169456
obj = -37.013024, rho = 0.257740
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.143573
obj = -43.408103, rho = 0.186884
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.118098
obj = -49.944602, rho = 0.203953
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.092699
obj = -57.367708, rho = 0.324611
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.962923, rho = -0.014448
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.361732, rho = -0.020783
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.910398, rho = -0.029896
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.647896, rho = -0.043004
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.601712, rho = -0.061858
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.752253, rho = -0.088980
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.055693, rho = -0.191290
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.852677
obj = -7.504003, rho = -0.213931
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.733925
obj = -9.165938, rho = -0.173874
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.621646
obj = -11.057212, rho = -0.170015
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.519684
obj = -13.387026, rho = -0.146668
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.440919
obj = -16.206417, rho = -0.082621
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.369513
obj = -19.575737, rho = -0.089248
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.320088
obj = -23.361508, rho = -0.113804
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.262168
obj = -27.554765, rho = -0.074512
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.211087
obj = -32.718618, rho = -0.081965
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.181151
obj = -39.042437, rho = 0.034974
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.151086
obj = -45.418211, rho = 0.188303
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 288
nu = 0.121372
obj = -52.386724, rho = 0.253401
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.103352
obj = -58.660673, rho = 0.385280
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.908545, rho = -0.912683
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.287059, rho = -0.874399
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.810322, rho = -0.819329
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.519125, rho = -0.740114
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.447896, rho = -0.626166
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 60% (60/100) (classification)
Accuracy = 58.1% (581/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.595998, rho = -0.462259
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 86% (86/100) (classification)
Accuracy = 82.3% (823/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -5.897687, rho = -0.407274
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.812564
obj = -7.390911, rho = -0.356811
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.726715
obj = -9.095201, rho = -0.267315
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.611347
obj = -11.069540, rho = -0.222199
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.522993
obj = -13.441806, rho = -0.142314
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.440275
obj = -16.286300, rho = -0.117536
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.380000
obj = -19.501753, rho = -0.043805
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.314077
obj = -23.179118, rho = -0.035453
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.262930
obj = -27.633438, rho = -0.011667
nSV = 28, nBSV = 25
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.217172
obj = -32.078446, rho = -0.091221
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.176567
obj = -37.069990, rho = -0.082157
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.142275
obj = -43.008578, rho = -0.161052
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.118447
obj = -48.688805, rho = -0.332273
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.092452
obj = -54.154790, rho = -0.413008
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.917163, rho = -0.932129
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.304892, rho = -0.902533
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.847221, rho = -0.859799
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -2.595472, rho = -0.798328
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -3.605870, rho = -0.709904
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -4.922868, rho = -0.582712
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 59% (59/100) (classification)
Accuracy = 59.9% (599/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.940000
obj = -6.535057, rho = -0.399365
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 90% (90/100) (classification)
Accuracy = 88.5% (885/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.897755
obj = -8.305944, rho = -0.217554
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800807
obj = -10.317954, rho = -0.223710
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.713597
obj = -12.540919, rho = -0.070936
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.593931
obj = -15.019901, rho = -0.067385
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.503169
obj = -17.949377, rho = -0.109712
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.412370
obj = -21.279282, rho = -0.097970
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.344341
obj = -25.273457, rho = -0.034085
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.286915
obj = -29.728009, rho = -0.038958
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.233160
obj = -34.514403, rho = -0.085910
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.194565
obj = -40.070945, rho = -0.070590
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.153952
obj = -45.664370, rho = -0.044432
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.124847
obj = -52.386479, rho = -0.049322
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.098768
obj = -58.875790, rho = -0.065917
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.916013, rho = -0.930708
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.302511, rho = -0.900327
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.842294, rho = -0.856626
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.585279, rho = -0.793764
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.584779, rho = -0.703339
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.879227, rho = -0.573268
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 76% (76/100) (classification)
Accuracy = 67.8% (678/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.444758, rho = -0.386168
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.876495
obj = -8.192791, rho = -0.299442
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 94% (94/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.780045
obj = -10.268104, rho = -0.254169
nSV = 81, nBSV = 75
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.685486
obj = -12.729497, rho = -0.197843
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 95% (95/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.583553
obj = -15.774183, rho = -0.177452
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 95% (95/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.492099
obj = -19.777968, rho = -0.206990
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 95% (95/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.431220
obj = -25.050416, rho = -0.259667
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 95% (95/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.378899
obj = -31.624273, rho = -0.238770
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.332704
obj = -40.005627, rho = -0.352295
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.288192
obj = -50.851390, rho = -0.438664
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.248181
obj = -65.662460, rho = -0.472452
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.225842
obj = -85.733022, rho = -0.611656
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.205527
obj = -111.640259, rho = -0.615633
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.194102
obj = -143.085771, rho = -0.782161
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.917082, rho = -0.933571
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.304723, rho = -0.904445
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.846871, rho = -0.862549
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.594749, rho = -0.802284
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.604373, rho = -0.715596
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.919771, rho = -0.590899
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 69% (69/100) (classification)
Accuracy = 62.4% (624/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.528648, rho = -0.411528
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 89.4% (894/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -8.385058, rho = -0.366007
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.797107
obj = -10.576342, rho = -0.313271
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.703303
obj = -13.167452, rho = -0.246173
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.615774
obj = -16.271080, rho = -0.265614
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.520000
obj = -20.016952, rho = -0.249492
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.450577
obj = -24.441272, rho = -0.189908
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.381457
obj = -29.795245, rho = -0.125372
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.330037
obj = -36.473020, rho = -0.078700
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 297
nu = 0.277336
obj = -43.861989, rho = -0.131005
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.236569
obj = -53.291567, rho = -0.219059
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.199122
obj = -63.612009, rho = -0.228592
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.167496
obj = -75.845511, rho = -0.215949
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*...*
optimization finished, #iter = 340
nu = 0.141405
obj = -87.703506, rho = -0.104464
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.900925, rho = -0.929925
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.283906, rho = -0.899201
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.821942, rho = -0.855006
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.569266, rho = -0.791433
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.589189, rho = -0.699987
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.942356, rho = -0.568447
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 55% (55/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.653062, rho = -0.379233
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 74% (74/100) (classification)
Accuracy = 77.8% (778/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.908691
obj = -8.629083, rho = -0.135285
nSV = 93, nBSV = 89
Total nSV = 93
Accuracy = 93% (93/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.816480
obj = -10.840879, rho = -0.009679
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.719608
obj = -13.516727, rho = 0.010328
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.632893
obj = -16.736779, rho = 0.034244
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.540169
obj = -20.529378, rho = 0.044215
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.466104
obj = -25.109355, rho = 0.009552
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.389669
obj = -30.624867, rho = 0.016914
nSV = 43, nBSV = 34
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.331006
obj = -37.733469, rho = -0.030307
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.282189
obj = -46.345256, rho = -0.146099
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.248689
obj = -57.044253, rho = -0.154901
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.207913
obj = -69.175347, rho = -0.246311
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.170895
obj = -85.219043, rho = -0.243228
nSV = 23, nBSV = 12
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.148162
obj = -106.909318, rho = -0.366621
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.896907, rho = 0.884466
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.275592, rho = 0.833810
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.804739, rho = 0.760944
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.533672, rho = 0.656130
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.515540, rho = 0.505360
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.789966, rho = 0.288485
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 66% (66/100) (classification)
Accuracy = 65.2% (652/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.337746, rho = -0.023479
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 91% (91/100) (classification)
Accuracy = 91% (910/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -8.100716, rho = -0.148212
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 93% (93/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.761038
obj = -10.260317, rho = -0.143672
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 95% (95/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.667631
obj = -12.903098, rho = -0.121507
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.586330
obj = -16.174885, rho = -0.177389
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.522758
obj = -20.132866, rho = -0.311701
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 95% (95/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.448608
obj = -24.907583, rho = -0.322967
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 95% (95/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.382552
obj = -30.801320, rho = -0.325631
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 95% (95/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.338894
obj = -38.038951, rho = -0.245443
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.283770
obj = -46.826842, rho = -0.187888
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.238933
obj = -58.307127, rho = -0.136125
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.209975
obj = -73.180791, rho = -0.050077
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.182073
obj = -90.570192, rho = 0.034651
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.155045
obj = -113.008425, rho = 0.132618
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.879691, rho = -0.946312
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.252582, rho = -0.922772
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.775274, rho = -0.888912
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -2.498804, rho = -0.840205
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.480935, rho = -0.770143
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.772369, rho = -0.669363
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 58% (58/100) (classification)
Accuracy = 55.2% (552/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.379016, rho = -0.524395
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 84% (84/100) (classification)
Accuracy = 82.3% (823/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -8.181972, rho = -0.345640
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.785149
obj = -10.226473, rho = -0.320900
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.692764
obj = -12.512937, rho = -0.230373
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.594346
obj = -15.126510, rho = -0.226700
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.500631
obj = -18.097761, rho = -0.188934
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.415727
obj = -21.679375, rho = -0.157521
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.343891
obj = -25.872551, rho = -0.145609
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.289847
obj = -30.935715, rho = -0.065210
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.239186
obj = -36.707831, rho = -0.071825
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.196936
obj = -43.814581, rho = -0.069898
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.164131
obj = -52.202488, rho = -0.044460
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.140173
obj = -61.382152, rho = -0.024605
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.119652
obj = -69.803243, rho = 0.216506
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -0.804157, rho = 0.922501
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.146747, rho = 0.888521
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -1.628863, rho = 0.839643
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -2.300259, rho = 0.769335
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -3.220291, rho = 0.668200
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -4.449074, rho = 0.522722
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 52.4% (524/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -6.020801, rho = 0.313460
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 75% (75/100) (classification)
Accuracy = 71.1% (711/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -7.876481, rho = 0.012446
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -9.900444, rho = -0.131560
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.666248
obj = -12.211896, rho = -0.092222
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.582897
obj = -14.842898, rho = -0.024381
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.485845
obj = -17.950007, rho = -0.002549
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.407661
obj = -21.610141, rho = 0.052850
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.351131
obj = -26.027185, rho = 0.101595
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.289668
obj = -31.041908, rho = 0.138399
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.241574
obj = -36.814176, rho = 0.143091
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.199198
obj = -43.560968, rho = 0.254657
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.169556
obj = -51.068315, rho = 0.217697
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.135806
obj = -59.087658, rho = 0.201584
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.107822
obj = -68.415165, rho = 0.214947
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.934026, rho = -0.923939
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.7% (467/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.327168, rho = -0.890589
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.7% (467/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.875168, rho = -0.842618
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.7% (467/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.627201, rho = -0.773614
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.7% (467/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.633977, rho = -0.674356
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.927022, rho = -0.531577
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 75% (75/100) (classification)
Accuracy = 71% (710/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.954863
obj = -6.466291, rho = -0.365797
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 90% (90/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.888564
obj = -8.236570, rho = -0.267504
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -10.319669, rho = -0.271372
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.680709
obj = -12.794317, rho = -0.317286
nSV = 72, nBSV = 64
Total nSV = 72
Accuracy = 96% (96/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.594697
obj = -15.876881, rho = -0.240734
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.507943
obj = -19.607328, rho = -0.238801
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.442014
obj = -24.020599, rho = -0.156051
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.373953
obj = -29.339686, rho = -0.223282
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.329770
obj = -35.591253, rho = -0.115627
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.270669
obj = -42.712713, rho = -0.063930
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.230801
obj = -51.357170, rho = -0.143484
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.190842
obj = -61.587714, rho = -0.132941
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.159518
obj = -73.384971, rho = -0.077607
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.135098
obj = -86.338951, rho = 0.006736
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.966373, rho = -0.025604
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.368870, rho = -0.036830
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.925168, rho = -0.052979
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.678457, rho = -0.076207
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.664948, rho = -0.109620
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.883097, rho = -0.157683
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -6.247869, rho = -0.218521
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.865181
obj = -7.799067, rho = -0.169316
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760754
obj = -9.590468, rho = -0.187918
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.645673
obj = -11.660763, rho = -0.188225
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.554118
obj = -14.061810, rho = -0.209130
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.477366
obj = -16.776890, rho = -0.123650
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.387391
obj = -19.755735, rho = -0.153072
nSV = 43, nBSV = 34
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.322365
obj = -23.299989, rho = -0.178555
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.256866
obj = -27.573565, rho = -0.164638
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.218270
obj = -32.529545, rho = -0.075146
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.178036
obj = -38.243884, rho = -0.037295
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.145821
obj = -44.312498, rho = -0.020535
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.118011
obj = -51.530563, rho = -0.112056
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.099607
obj = -59.148499, rho = -0.103989
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -0.859355, rho = 0.923543
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.223119, rho = 0.890021
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -1.732455, rho = 0.841800
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -2.436305, rho = 0.772438
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -3.389159, rho = 0.672663
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -4.636475, rho = 0.529142
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 57% (57/100) (classification)
Accuracy = 54.6% (546/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -6.175514, rho = 0.322694
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 84% (84/100) (classification)
Accuracy = 80.3% (803/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.869017, rho = 0.070085
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.766293
obj = -9.630358, rho = 0.019926
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.661325
obj = -11.650577, rho = 0.008567
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.568523
obj = -13.864661, rho = -0.025415
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.467497
obj = -16.232882, rho = -0.017903
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.387662
obj = -18.822412, rho = 0.051741
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.310711
obj = -21.604205, rho = 0.085733
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.247094
obj = -24.791994, rho = 0.045898
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.195244
obj = -28.525515, rho = 0.048829
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.154268
obj = -33.195515, rho = 0.061258
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.127071
obj = -39.205441, rho = 0.175321
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.102760
obj = -45.980959, rho = 0.097471
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.086716
obj = -53.399392, rho = -0.236431
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.953923, rho = -0.899566
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.355723, rho = -0.855530
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.916110, rho = -0.792188
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.685814, rho = -0.701072
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.717713, rho = -0.570008
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 53.1% (531/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.046280, rho = -0.381478
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 79% (79/100) (classification)
Accuracy = 81.9% (819/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.635050, rho = -0.110286
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -8.397254, rho = -0.153135
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.820949
obj = -10.329157, rho = -0.079597
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.701342
obj = -12.531860, rho = -0.036891
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.606274
obj = -15.051174, rho = -0.054341
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.504450
obj = -17.825717, rho = -0.017123
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.411430
obj = -21.043488, rho = -0.035197
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.343910
obj = -24.797903, rho = -0.064417
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.275815
obj = -29.207123, rho = -0.075964
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.229469
obj = -34.411097, rho = -0.110501
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.184210
obj = -40.474598, rho = -0.099754
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.162783
obj = -47.029423, rho = -0.053892
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.135877
obj = -50.510748, rho = 0.063419
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.099893
obj = -52.766494, rho = 0.085535
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.857339, rho = -0.945065
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.218948, rho = -0.920979
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.723823, rho = -0.886333
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -2.418445, rho = -0.836495
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.352205, rho = -0.764807
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.560011, rho = -0.661686
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 68% (68/100) (classification)
Accuracy = 61.5% (615/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -6.017301, rho = -0.513353
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 94% (94/100) (classification)
Accuracy = 86% (860/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -7.579984, rho = -0.365770
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.730969
obj = -9.339838, rho = -0.353793
nSV = 76, nBSV = 71
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.632764
obj = -11.407387, rho = -0.325093
nSV = 67, nBSV = 60
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.540000
obj = -13.804417, rho = -0.317946
nSV = 55, nBSV = 53
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.458665
obj = -16.475418, rho = -0.250157
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.384262
obj = -19.541375, rho = -0.199618
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.307590
obj = -23.277945, rho = -0.212627
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.255141
obj = -28.025688, rho = -0.325185
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.216001
obj = -33.661358, rho = -0.285021
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*..*
optimization finished, #iter = 238
nu = 0.180959
obj = -40.212100, rho = -0.249838
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.153244
obj = -47.754541, rho = -0.251968
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 114
nu = 0.129384
obj = -56.143665, rho = -0.367055
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.102377
obj = -65.272968, rho = -0.363424
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.933994, rho = 0.892156
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.327102, rho = 0.844872
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.875033, rho = 0.776855
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.626920, rho = 0.679018
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -3.633396, rho = 0.538283
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -4.925820, rho = 0.335843
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 69% (69/100) (classification)
Accuracy = 65.2% (652/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.958489
obj = -6.463522, rho = 0.059153
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.893276
obj = -8.180179, rho = 0.031034
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.790942
obj = -10.172169, rho = 0.022244
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.685743
obj = -12.405295, rho = 0.040299
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.588690
obj = -14.980620, rho = -0.002275
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.501882
obj = -17.870303, rho = -0.071112
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.413432
obj = -21.190904, rho = -0.109150
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.339908
obj = -25.032982, rho = -0.123490
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.279342
obj = -29.670643, rho = -0.140730
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.228456
obj = -35.257693, rho = -0.162533
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.188946
obj = -42.080599, rho = -0.157687
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.154368
obj = -50.559747, rho = -0.104425
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.128248
obj = -61.232742, rho = -0.068260
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.108652
obj = -74.217058, rho = -0.021974
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.950972, rho = -0.893603
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.349619, rho = -0.846954
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.903479, rho = -0.779850
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.659679, rho = -0.683326
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.663637, rho = -0.544480
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 67% (67/100) (classification)
Accuracy = 64.7% (647/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.934389, rho = -0.344757
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 93% (93/100) (classification)
Accuracy = 91.4% (914/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.460839, rho = -0.353902
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 91% (91/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.856386
obj = -8.342432, rho = -0.315436
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 94% (94/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.785829
obj = -10.649273, rho = -0.351537
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.703752
obj = -13.346929, rho = -0.240387
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.620000
obj = -16.420050, rho = -0.258012
nSV = 63, nBSV = 61
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.523604
obj = -20.153748, rho = -0.274582
nSV = 58, nBSV = 50
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.451693
obj = -24.869510, rho = -0.285282
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.385516
obj = -30.746246, rho = -0.346103
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.324404
obj = -38.264361, rho = -0.337811
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.289737
obj = -47.857084, rho = -0.482471
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.257735
obj = -58.962282, rho = -0.447444
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.212880
obj = -71.700647, rho = -0.335868
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*....*
optimization finished, #iter = 416
nu = 0.178219
obj = -88.861340, rho = -0.268177
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.154858
obj = -110.951751, rho = -0.143659
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -0.839090, rho = -0.943432
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -1.193801, rho = -0.918630
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.689937, rho = -0.882482
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.374429, rho = -0.830956
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.298673, rho = -0.756838
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.503250, rho = -0.650224
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 62% (62/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -5.977537, rho = -0.496865
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 90% (90/100) (classification)
Accuracy = 82.6% (826/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.833507
obj = -7.580600, rho = -0.327480
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.735166
obj = -9.348153, rho = -0.292550
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.645784
obj = -11.390556, rho = -0.181913
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.552959
obj = -13.523875, rho = -0.215738
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.463967
obj = -15.847574, rho = -0.186911
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.375080
obj = -18.303048, rho = -0.197159
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.293267
obj = -21.326933, rho = -0.230397
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.244123
obj = -25.094432, rho = -0.273097
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.196722
obj = -29.106897, rho = -0.235844
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.162460
obj = -33.859287, rho = -0.269242
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.130641
obj = -39.182302, rho = -0.300273
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.105253
obj = -44.710417, rho = -0.268798
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.081374
obj = -51.564374, rho = -0.320826
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.936355, rho = 0.883639
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.331988, rho = 0.832620
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.885141, rho = 0.759232
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.647836, rho = 0.653667
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.676674, rho = 0.501818
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -5.015368, rho = 0.283390
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 65% (65/100) (classification)
Accuracy = 68.3% (683/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.648771, rho = -0.030808
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 92% (92/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.900199
obj = -8.489637, rho = -0.189496
nSV = 93, nBSV = 89
Total nSV = 93
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.819267
obj = -10.614089, rho = -0.144839
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.715797
obj = -13.089613, rho = -0.105134
nSV = 74, nBSV = 68
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.620000
obj = -16.035686, rho = -0.092950
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.527523
obj = -19.324560, rho = -0.120383
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.442994
obj = -23.198432, rho = -0.112595
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.366312
obj = -27.897440, rho = -0.039091
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.311669
obj = -33.683636, rho = -0.089169
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.257114
obj = -40.593560, rho = -0.041661
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.219457
obj = -48.561087, rho = -0.227096
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.180632
obj = -57.674671, rho = -0.153864
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.147332
obj = -69.592472, rho = -0.081517
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*....*
optimization finished, #iter = 583
nu = 0.122802
obj = -84.465075, rho = -0.095242
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.963190, rho = -0.036489
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.362285, rho = -0.052488
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.911542, rho = -0.075502
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.650264, rho = -0.108605
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.606613, rho = -0.156223
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.762394, rho = -0.224720
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.919334
obj = -6.108245, rho = -0.229848
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.826496
obj = -7.755169, rho = -0.235187
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.741920
obj = -9.745015, rho = -0.151559
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.655065
obj = -11.984527, rho = -0.034870
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.566398
obj = -14.685166, rho = -0.017757
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.478694
obj = -17.783765, rho = -0.024292
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.403758
obj = -21.524166, rho = -0.051773
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.344329
obj = -26.004823, rho = -0.077528
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.284031
obj = -31.353111, rho = -0.077607
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.233538
obj = -38.299242, rho = -0.093490
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.205539
obj = -47.089790, rho = -0.033389
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.180219
obj = -56.565588, rho = -0.234079
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.149120
obj = -67.092357, rho = -0.156791
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.121458
obj = -79.378847, rho = -0.196856
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.950207, rho = 0.835522
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.348036, rho = 0.763406
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.900203, rho = 0.659672
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.652902, rho = 0.510455
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.649614, rho = 0.295814
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 61% (61/100) (classification)
Accuracy = 57.4% (574/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.905373, rho = -0.012936
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 91% (91/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.372455, rho = -0.243707
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 95% (95/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.873598
obj = -8.066275, rho = -0.173665
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780403
obj = -9.982717, rho = -0.138371
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.679929
obj = -12.114512, rho = -0.144906
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.570777
obj = -14.616461, rho = -0.123092
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.483364
obj = -17.504472, rho = -0.121130
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.399228
obj = -21.006527, rho = -0.174869
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.341282
obj = -25.201207, rho = -0.113177
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.284395
obj = -29.704460, rho = -0.147475
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.232825
obj = -35.028195, rho = -0.231749
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.191855
obj = -41.056767, rho = -0.265165
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.152961
obj = -48.257613, rho = -0.236578
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.127858
obj = -57.227599, rho = -0.173293
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.111268
obj = -65.836854, rho = 0.021615
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.948602, rho = 0.863150
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.344713, rho = 0.803149
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.893329, rho = 0.716839
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.638677, rho = 0.592687
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.620180, rho = 0.414101
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 53% (53/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.844470, rho = 0.157214
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 82% (82/100) (classification)
Accuracy = 78.2% (782/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.969135
obj = -6.219183, rho = -0.133375
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.872950
obj = -7.698472, rho = -0.153285
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -9.334354, rho = -0.163203
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.646576
obj = -11.148546, rho = -0.150947
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.536794
obj = -13.198673, rho = -0.158807
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.449367
obj = -15.583188, rho = -0.113957
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.362752
obj = -18.150036, rho = -0.160242
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.296285
obj = -21.246321, rho = -0.157153
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.241568
obj = -24.649158, rho = -0.091732
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.191536
obj = -28.763812, rho = -0.093781
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.160148
obj = -33.146258, rho = -0.026145
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.125141
obj = -38.445746, rho = 0.065084
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.100047
obj = -45.081340, rho = 0.084125
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.081896
obj = -53.658409, rho = 0.037285
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.933416, rho = -0.919464
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.325905, rho = -0.884153
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.872556, rho = -0.833360
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.621795, rho = -0.760296
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.622792, rho = -0.655198
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 55% (55/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.903878, rho = -0.504019
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 79% (79/100) (classification)
Accuracy = 77.1% (771/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.428114, rho = -0.343575
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.871678
obj = -8.206716, rho = -0.287339
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.783687
obj = -10.299897, rho = -0.234069
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.690359
obj = -12.775825, rho = -0.222661
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.600000
obj = -15.712512, rho = -0.283713
nSV = 61, nBSV = 59
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.506516
obj = -19.254204, rho = -0.232907
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.438236
obj = -23.582651, rho = -0.187650
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.369921
obj = -28.650292, rho = -0.140080
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.308040
obj = -34.934889, rho = -0.146730
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.271855
obj = -42.112109, rho = -0.262606
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.221059
obj = -50.725383, rho = -0.250814
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.190625
obj = -61.292545, rho = -0.256430
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.160344
obj = -72.879452, rho = -0.362156
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*...*
optimization finished, #iter = 362
nu = 0.135046
obj = -85.940587, rho = -0.307689
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.879160, rho = -0.929424
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.251483, rho = -0.898479
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.773000, rho = -0.853740
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -2.494098, rho = -0.789613
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.471198, rho = -0.697368
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.752223, rho = -0.565356
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 56% (56/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.337332, rho = -0.374786
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 89% (89/100) (classification)
Accuracy = 85% (850/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.885030
obj = -8.089060, rho = -0.161708
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.785840
obj = -9.939555, rho = -0.076700
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.671478
obj = -12.107769, rho = -0.098095
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.570356
obj = -14.717795, rho = -0.083935
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.485025
obj = -17.790616, rho = -0.150408
nSV = 51, nBSV = 48
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.420824
obj = -21.143125, rho = -0.134631
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.340624
obj = -24.783085, rho = -0.165176
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.285061
obj = -29.021364, rho = -0.110244
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.231949
obj = -33.806363, rho = -0.134442
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.185549
obj = -38.651686, rho = -0.165367
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.145182
obj = -44.741576, rho = -0.200106
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.116238
obj = -52.639460, rho = -0.167600
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.099377
obj = -61.622251, rho = -0.437853
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.916153, rho = -0.927934
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.302801, rho = -0.896336
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.842895, rho = -0.850884
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.586522, rho = -0.785505
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.587350, rho = -0.691459
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.884547, rho = -0.556180
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 64% (64/100) (classification)
Accuracy = 62.6% (626/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.455765, rho = -0.361587
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 92.2% (922/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.892306
obj = -8.169684, rho = -0.236060
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.808504
obj = -10.039244, rho = -0.157020
nSV = 83, nBSV = 77
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.680000
obj = -12.177163, rho = -0.144945
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.575113
obj = -14.706841, rho = -0.092863
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.482562
obj = -17.763515, rho = -0.103419
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.409690
obj = -21.415156, rho = -0.163221
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.334045
obj = -25.909044, rho = -0.200015
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.280817
obj = -31.688365, rho = -0.230595
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.241055
obj = -38.862018, rho = -0.269659
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.202130
obj = -47.681551, rho = -0.292634
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.178865
obj = -58.182201, rho = -0.160780
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.150889
obj = -69.706446, rho = -0.125424
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.129960
obj = -83.529029, rho = -0.022732
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.950697, rho = 0.880825
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.349050, rho = 0.828573
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.902301, rho = 0.753411
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.657242, rho = 0.645294
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.658595, rho = 0.489774
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.923955, rho = 0.266065
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 78% (78/100) (classification)
Accuracy = 77% (770/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.381943, rho = -0.037346
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -7.995870, rho = -0.070000
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -9.843577, rho = -0.006159
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.672687
obj = -11.864772, rho = -0.053180
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.559245
obj = -14.262876, rho = 0.002428
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.480591
obj = -17.145826, rho = -0.003602
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.404982
obj = -20.211835, rho = -0.058094
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.325774
obj = -23.717352, rho = -0.029735
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.264120
obj = -27.925357, rho = 0.014406
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.217435
obj = -33.244689, rho = 0.080531
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.182991
obj = -39.251454, rho = 0.135482
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.148497
obj = -46.106324, rho = 0.156924
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*..*
optimization finished, #iter = 241
nu = 0.124242
obj = -53.487895, rho = 0.068029
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.100503
obj = -61.345284, rho = 0.071124
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -0.803569, rho = 0.938204
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.145532, rho = 0.911109
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.626349, rho = 0.872135
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -2.295056, rho = 0.816073
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -3.209525, rho = 0.735430
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -4.426797, rho = 0.619429
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -5.974707, rho = 0.452568
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 69% (69/100) (classification)
Accuracy = 61.3% (613/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -7.781111, rho = 0.212853
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 93% (93/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.754823
obj = -9.701675, rho = 0.059559
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.662866
obj = -11.827359, rho = 0.006153
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.563747
obj = -14.195664, rho = 0.027531
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.471665
obj = -16.920709, rho = 0.032691
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.386655
obj = -20.181142, rho = -0.016916
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.330349
obj = -23.933312, rho = -0.038806
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.267060
obj = -28.203906, rho = -0.053203
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.219981
obj = -33.136901, rho = -0.092084
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.182486
obj = -39.173295, rho = -0.115364
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.151672
obj = -45.191036, rho = -0.056690
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.121458
obj = -51.731806, rho = -0.026856
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.097395
obj = -59.190540, rho = 0.040961
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -0.686217, rho = -0.963661
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -0.978397, rho = -0.947728
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -1.389389, rho = -0.924809
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -1.961351, rho = -0.891841
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -2.744300, rho = -0.844419
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -3.788206, rho = -0.776204
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -5.119464, rho = -0.678081
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 74% (74/100) (classification)
Accuracy = 60.8% (608/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.700000
obj = -6.681939, rho = -0.536936
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 93% (93/100) (classification)
Accuracy = 82.1% (821/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.660000
obj = -8.316840, rho = -0.414927
nSV = 67, nBSV = 65
Total nSV = 67
Accuracy = 95% (95/100) (classification)
Accuracy = 91.8% (918/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.567801
obj = -10.051967, rho = -0.407124
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 96% (96/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.487201
obj = -11.995091, rho = -0.375273
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.402753
obj = -14.240096, rho = -0.381905
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.328856
obj = -16.737444, rho = -0.373269
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.267015
obj = -19.879666, rho = -0.328662
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.219830
obj = -23.837147, rho = -0.300441
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.185950
obj = -28.524437, rho = -0.275157
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.154780
obj = -33.958066, rho = -0.243583
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 291
nu = 0.132504
obj = -39.178920, rho = -0.069191
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 353
nu = 0.106097
obj = -44.821538, rho = -0.078863
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*.*
optimization finished, #iter = 304
nu = 0.089904
obj = -49.764712, rho = -0.130461
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -0.819740, rho = -0.937616
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -1.166377, rho = -0.910264
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -1.651337, rho = -0.870920
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -2.320659, rho = -0.814324
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -3.224959, rho = -0.732915
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -4.404729, rho = -0.615812
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 62% (62/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -5.851363, rho = -0.447364
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 87% (87/100) (classification)
Accuracy = 77.6% (776/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.804868
obj = -7.455035, rho = -0.294585
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 92.5% (925/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.721549
obj = -9.235290, rho = -0.264343
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.636792
obj = -11.252432, rho = -0.171509
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.540827
obj = -13.502114, rho = -0.229432
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.454564
obj = -16.028833, rho = -0.253821
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.372336
obj = -18.856097, rho = -0.274700
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.302246
obj = -22.202912, rho = -0.236187
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.249135
obj = -26.319895, rho = -0.199652
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.201074
obj = -31.449070, rho = -0.173770
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.171855
obj = -37.689466, rho = -0.084537
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.141971
obj = -44.745638, rho = -0.029824
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.121355
obj = -52.072978, rho = -0.163244
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.097468
obj = -59.692771, rho = -0.218616
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.881266, rho = -0.932485
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.255842, rho = -0.902883
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.782019, rho = -0.860301
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -2.512761, rho = -0.799051
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.509815, rho = -0.710944
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.832124, rho = -0.584208
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.502659, rho = -0.401904
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 75% (75/100) (classification)
Accuracy = 78.6% (786/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.882416
obj = -8.435250, rho = -0.236015
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.806865
obj = -10.626137, rho = -0.127283
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.712611
obj = -13.061613, rho = -0.146737
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.613102
obj = -15.864740, rho = -0.212143
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.526143
obj = -19.103555, rho = -0.133219
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.438645
obj = -22.788005, rho = -0.104381
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.360299
obj = -27.308806, rho = -0.150986
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.309715
obj = -32.724925, rho = -0.160620
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.253542
obj = -38.740412, rho = -0.118384
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.208965
obj = -46.143088, rho = -0.047614
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.170199
obj = -54.966258, rho = 0.018021
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.139388
obj = -66.515549, rho = 0.020433
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.114587
obj = -81.676689, rho = 0.070515
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.916067, rho = -0.920243
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.302622, rho = -0.885273
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.842523, rho = -0.834971
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.585753, rho = -0.762614
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.585760, rho = -0.658532
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.4% (524/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.881257, rho = -0.508816
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 72% (72/100) (classification)
Accuracy = 72.4% (724/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.448959, rho = -0.293456
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.874925
obj = -8.199807, rho = -0.219371
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.787304
obj = -10.259693, rho = -0.175374
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.700000
obj = -12.568835, rho = -0.108199
nSV = 73, nBSV = 66
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.593222
obj = -15.178238, rho = -0.042803
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.502929
obj = -18.246462, rho = -0.103670
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.425477
obj = -21.767492, rho = -0.069004
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.353700
obj = -25.573000, rho = -0.048349
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.290334
obj = -29.938589, rho = -0.057755
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.234371
obj = -34.786931, rho = -0.034508
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.187821
obj = -40.647379, rho = -0.002859
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.158431
obj = -47.521085, rho = -0.114197
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.126109
obj = -54.819069, rho = -0.102673
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.103200
obj = -63.570584, rho = -0.180477
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.660000
obj = -0.651250, rho = 0.966582
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.660000
obj = -0.931273, rho = 0.951930
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.660000
obj = -1.328171, rho = 0.930854
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.660000
obj = -1.886882, rho = 0.900536
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.660000
obj = -2.665301, rho = 0.856927
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.660000
obj = -3.732753, rho = 0.794196
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.660000
obj = -5.160087, rho = 0.703962
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 67% (67/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.660000
obj = -6.989475, rho = 0.574163
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 69% (69/100) (classification)
Accuracy = 57.3% (573/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.660000
obj = -9.157966, rho = 0.383330
nSV = 67, nBSV = 66
Total nSV = 67
Accuracy = 89% (89/100) (classification)
Accuracy = 80.2% (802/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.640000
obj = -11.474589, rho = 0.180104
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.545564
obj = -13.814562, rho = 0.117210
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.457024
obj = -16.496516, rho = 0.072091
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.377890
obj = -19.598306, rho = -0.010165
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.310620
obj = -23.487154, rho = 0.012745
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.265135
obj = -28.277836, rho = -0.034782
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.216446
obj = -33.794988, rho = 0.010603
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.180121
obj = -40.395738, rho = 0.041860
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.152824
obj = -48.367925, rho = 0.113887
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.122539
obj = -58.111119, rho = 0.128333
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.106239
obj = -70.833154, rho = 0.253776
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.964645, rho = -0.052644
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 89% (89/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.365296, rho = -0.075725
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 89% (89/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.917772, rho = -0.108927
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 89% (89/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.663154, rho = -0.156686
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 89% (89/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.633284, rho = -0.225385
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 89% (89/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.817582, rho = -0.324205
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 89% (89/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.937132
obj = -6.200093, rho = -0.313418
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 91% (91/100) (classification)
Accuracy = 89.6% (896/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.838428
obj = -7.862638, rho = -0.275696
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 92% (92/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.742669
obj = -9.921454, rho = -0.257735
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 94% (94/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.662340
obj = -12.369333, rho = -0.271442
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.578911
obj = -15.215475, rho = -0.185767
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.486157
obj = -18.691403, rho = -0.186914
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.428085
obj = -22.897581, rho = -0.152808
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.360971
obj = -27.627050, rho = -0.223404
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.302384
obj = -33.383142, rho = -0.284714
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.266666
obj = -40.061092, rho = -0.247386
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.219093
obj = -46.683143, rho = -0.206481
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*........*
optimization finished, #iter = 819
nu = 0.175942
obj = -54.566143, rho = -0.248472
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.138443
obj = -64.982917, rho = -0.250772
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.119361
obj = -78.576084, rho = -0.229374
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.862516, rho = 0.934093
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.229658, rho = 0.905196
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.745985, rho = 0.863629
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -2.464301, rho = 0.803837
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.447087, rho = 0.717829
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.756335, rho = 0.594111
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.423522, rho = 0.416149
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 76% (76/100) (classification)
Accuracy = 70.1% (701/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.374544, rho = 0.160160
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 91% (91/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.787093
obj = -10.585117, rho = 0.084458
nSV = 82, nBSV = 76
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.708965
obj = -13.247773, rho = 0.109217
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.612544
obj = -16.407116, rho = 0.129339
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.535109
obj = -20.216194, rho = 0.090240
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.454593
obj = -24.736865, rho = 0.031843
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.381593
obj = -30.405595, rho = 0.003865
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.328439
obj = -37.544312, rho = -0.114737
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.281055
obj = -46.118170, rho = -0.077263
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.253054
obj = -56.182803, rho = 0.025331
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*....*
optimization finished, #iter = 431
nu = 0.211673
obj = -66.554030, rho = 0.064524
nSV = 29, nBSV = 17
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.179239
obj = -77.956279, rho = 0.066815
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.143387
obj = -91.021012, rho = 0.110863
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.913712, rho = 0.876382
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.297750, rho = 0.822181
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.832443, rho = 0.744217
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.564896, rho = 0.632069
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.542604, rho = 0.470749
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.791961, rho = 0.238700
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 78% (78/100) (classification)
Accuracy = 72.4% (724/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.271734, rho = -0.011408
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 92% (92/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.862612
obj = -7.928346, rho = -0.116376
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.773123
obj = -9.828371, rho = -0.012316
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.671715
obj = -11.951468, rho = -0.012234
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.552980
obj = -14.555358, rho = -0.058473
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.469487
obj = -17.838406, rho = -0.056602
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.407389
obj = -21.651681, rho = -0.159550
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.352002
obj = -25.895614, rho = -0.247642
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.293790
obj = -30.586290, rho = -0.183389
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.245460
obj = -35.788763, rho = -0.229197
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.197211
obj = -41.207779, rho = -0.200265
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.160139
obj = -47.233664, rho = -0.299833
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.128229
obj = -53.541272, rho = -0.293978
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*..*
optimization finished, #iter = 328
nu = 0.103530
obj = -59.366563, rho = -0.372592
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.912488, rho = 0.871913
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.295218, rho = 0.815753
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.827204, rho = 0.734970
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.554055, rho = 0.618767
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -3.520172, rho = 0.451616
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.745547, rho = 0.211177
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 84% (84/100) (classification)
Accuracy = 76.9% (769/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.934950
obj = -6.168617, rho = -0.101915
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.795607, rho = -0.080079
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.752350
obj = -9.653866, rho = -0.093129
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.660000
obj = -11.790545, rho = -0.136314
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.560000
obj = -14.146410, rho = -0.188229
nSV = 57, nBSV = 55
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.468226
obj = -16.907874, rho = -0.162599
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.391944
obj = -20.158167, rho = -0.108555
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.316913
obj = -24.117231, rho = -0.096179
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.268942
obj = -29.101824, rho = -0.104203
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.219040
obj = -35.257572, rho = -0.137612
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.184118
obj = -43.033053, rho = -0.177893
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.156327
obj = -52.829222, rho = -0.119518
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.132439
obj = -65.214803, rho = -0.176397
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.118275
obj = -79.710445, rho = -0.182235
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -0.862313, rho = 0.914498
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -1.229240, rho = 0.877010
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -1.745119, rho = 0.823085
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -2.462509, rho = 0.745516
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -3.443380, rho = 0.633938
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -4.748665, rho = 0.473438
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 57% (57/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -6.407651, rho = 0.242567
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 83% (83/100) (classification)
Accuracy = 73.1% (731/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -8.383143, rho = 0.028260
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 92% (92/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.791138
obj = -10.638425, rho = -0.085112
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 94% (94/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.700339
obj = -13.370267, rho = -0.065498
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 95% (95/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.601896
obj = -16.782636, rho = -0.085758
nSV = 65, nBSV = 58
Total nSV = 65
Accuracy = 95% (95/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.529886
obj = -21.098414, rho = -0.011208
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.474818
obj = -26.250366, rho = 0.058010
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.403348
obj = -32.583857, rho = -0.000922
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.348434
obj = -40.690803, rho = 0.043458
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.300532
obj = -50.871344, rho = 0.097235
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.261204
obj = -63.712135, rho = 0.084446
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*..*.*
optimization finished, #iter = 289
nu = 0.225540
obj = -80.223637, rho = 0.049265
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.198094
obj = -101.359890, rho = 0.078230
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.174535
obj = -127.356698, rho = 0.260139
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -0.860856, rho = 0.911041
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.226223, rho = 0.872037
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.738878, rho = 0.815932
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -2.449595, rho = 0.735227
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -3.416658, rho = 0.619137
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -4.693373, rho = 0.452148
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 59% (59/100) (classification)
Accuracy = 55.1% (551/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -6.293246, rho = 0.211943
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 82% (82/100) (classification)
Accuracy = 79.1% (791/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.128469, rho = -0.025382
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 92% (92/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.775853
obj = -10.206500, rho = -0.031296
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 94% (94/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.675098
obj = -12.654698, rho = -0.014701
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 95% (95/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.600000
obj = -15.585335, rho = -0.001763
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.503620
obj = -19.014833, rho = 0.025566
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.421739
obj = -23.387915, rho = 0.024361
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.366313
obj = -28.817484, rho = -0.087851
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.315080
obj = -35.212806, rho = -0.017573
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.264364
obj = -43.168006, rho = -0.082025
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.228136
obj = -52.708797, rho = -0.016766
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.193096
obj = -64.224454, rho = 0.052986
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.162181
obj = -78.674111, rho = -0.022026
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.141488
obj = -95.562183, rho = -0.171053
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.916542, rho = -0.920411
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.303605, rho = -0.885515
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.844559, rho = -0.835319
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.589965, rho = -0.763115
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.594474, rho = -0.659253
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.899288, rho = -0.509852
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 64% (64/100) (classification)
Accuracy = 68.2% (682/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.486267, rho = -0.294947
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 97% (97/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.883924
obj = -8.282619, rho = -0.169954
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800656
obj = -10.348431, rho = -0.124739
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.701919
obj = -12.683787, rho = -0.059009
nSV = 74, nBSV = 68
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.595617
obj = -15.412419, rho = -0.125129
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.500000
obj = -18.799046, rho = -0.088764
nSV = 51, nBSV = 49
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.435494
obj = -22.639350, rho = -0.058950
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.370129
obj = -26.700618, rho = -0.102314
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.303539
obj = -31.276770, rho = -0.147573
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.243472
obj = -36.524994, rho = -0.143722
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.199435
obj = -42.843320, rho = -0.206829
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.159533
obj = -50.718180, rho = -0.208161
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.131726
obj = -60.658312, rho = -0.262022
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.110480
obj = -71.978061, rho = -0.331392
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949041, rho = -0.909639
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.345623, rho = -0.870020
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.895210, rho = -0.813031
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.642570, rho = -0.731054
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.628235, rho = -0.613134
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 56% (56/100) (classification)
Accuracy = 57.3% (573/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.861138, rho = -0.443513
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 85% (850/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.948308
obj = -6.269508, rho = -0.302152
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.871785
obj = -7.902263, rho = -0.225541
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.773759
obj = -9.751544, rho = -0.183514
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.664770
obj = -11.857291, rho = -0.222346
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.558055
obj = -14.359498, rho = -0.203976
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.471007
obj = -17.308406, rho = -0.195258
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.392920
obj = -20.857304, rho = -0.203908
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.322645
obj = -25.397963, rho = -0.239619
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.268680
obj = -31.470935, rho = -0.251087
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.226649
obj = -39.612214, rho = -0.289162
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.200010
obj = -50.614379, rho = -0.346524
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.183122
obj = -64.099521, rho = -0.376602
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.161267
obj = -79.632905, rho = -0.429158
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.136452
obj = -99.298435, rho = -0.480724
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -0.912710, rho = -0.933562
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.295676, rho = -0.904432
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.828153, rho = -0.862531
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -2.556018, rho = -0.802257
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -3.524234, rho = -0.715557
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -4.753952, rho = -0.590843
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 75% (75/100) (classification)
Accuracy = 69.9% (699/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.931568
obj = -6.186314, rho = -0.422001
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 95% (95/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.848177
obj = -7.810882, rho = -0.377861
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760000
obj = -9.665439, rho = -0.280558
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.661781
obj = -11.699389, rho = -0.304039
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.549402
obj = -14.108159, rho = -0.296687
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.459451
obj = -17.037562, rho = -0.324661
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.393451
obj = -20.571108, rho = -0.369668
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.335611
obj = -24.406299, rho = -0.447078
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.275216
obj = -28.613360, rho = -0.463258
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.221997
obj = -33.710332, rho = -0.404174
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.182473
obj = -39.750379, rho = -0.448509
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.146917
obj = -47.165021, rho = -0.447237
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.122713
obj = -56.387536, rho = -0.544446
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.102491
obj = -67.169962, rho = -0.729963
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.968772, rho = -0.023292
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.373834, rho = -0.033504
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.935439, rho = -0.048194
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.699709, rho = -0.069324
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.708920, rho = -0.099719
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.974083, rho = -0.143441
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.958488
obj = -6.445045, rho = -0.135427
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.881459
obj = -8.210668, rho = -0.087737
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.803267
obj = -10.225425, rho = 0.004374
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.718173
obj = -12.293468, rho = -0.087364
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.598324
obj = -14.465158, rho = -0.076209
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.486001
obj = -16.855826, rho = -0.102111
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.387272
obj = -19.849539, rho = -0.129510
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.318799
obj = -23.604921, rho = -0.078279
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.265082
obj = -27.790721, rho = 0.018761
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.216152
obj = -32.993521, rho = 0.073977
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.176545
obj = -39.160856, rho = 0.129931
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.145137
obj = -46.789007, rho = 0.105898
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.120579
obj = -56.276760, rho = 0.047592
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.098828
obj = -67.766984, rho = 0.040026
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -0.840257, rho = -0.933633
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.196216, rho = -0.904535
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.694932, rho = -0.862678
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.384766, rho = -0.802470
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.320061, rho = -0.715863
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.547505, rho = -0.591283
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 61% (61/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -6.069105, rho = -0.412081
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 84% (84/100) (classification)
Accuracy = 82.6% (826/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -7.829604, rho = -0.269150
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 94% (94/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -9.815799, rho = -0.184335
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 95% (95/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.662352
obj = -12.000960, rho = -0.116359
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.563790
obj = -14.537260, rho = -0.071007
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.475330
obj = -17.714455, rho = -0.045613
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.404937
obj = -21.386485, rho = 0.001457
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.338452
obj = -25.887871, rho = -0.052064
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.274658
obj = -31.709405, rho = -0.069789
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.233793
obj = -39.681489, rho = -0.038541
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.203998
obj = -49.859746, rho = -0.106188
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.182213
obj = -61.684893, rho = -0.195971
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.154415
obj = -75.726146, rho = -0.193376
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.132608
obj = -93.039828, rho = -0.154719
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.952485, rho = -0.901899
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.352750, rho = -0.858886
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.909957, rho = -0.797015
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.673083, rho = -0.708016
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.691370, rho = -0.579996
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 53% (53/100) (classification)
Accuracy = 54.7% (547/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.991773, rho = -0.395845
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 84% (84/100) (classification)
Accuracy = 80.5% (805/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.960000
obj = -6.528767, rho = -0.168470
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.374214, rho = -0.110436
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.807026
obj = -10.481261, rho = -0.025086
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.717518
obj = -12.912863, rho = -0.079319
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.613252
obj = -15.545881, rho = -0.098690
nSV = 65, nBSV = 57
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.510017
obj = -18.743453, rho = -0.062293
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.422440
obj = -22.598848, rho = -0.061437
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.353282
obj = -27.528047, rho = -0.077933
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.311426
obj = -33.412898, rho = -0.022344
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.251728
obj = -40.212298, rho = 0.007292
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.212745
obj = -48.632278, rho = 0.061166
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.178981
obj = -59.262800, rho = 0.005536
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.150684
obj = -72.048568, rho = 0.037263
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.125174
obj = -88.631888, rho = 0.067142
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.967602, rho = -0.049715
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.371415, rho = -0.071513
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.930433, rho = -0.102868
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.689352, rho = -0.147971
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.687491, rho = -0.212848
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.929743, rho = -0.306172
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.377065, rho = -0.339940
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 92% (92/100) (classification)
Accuracy = 92.5% (925/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.868418
obj = -8.060655, rho = -0.292871
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -10.024482, rho = -0.306355
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.665617
obj = -12.332325, rho = -0.350494
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.578299
obj = -15.197470, rho = -0.275803
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.499880
obj = -18.447795, rho = -0.315952
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.421133
obj = -22.256269, rho = -0.264523
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.351012
obj = -26.749256, rho = -0.295766
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.299528
obj = -32.380057, rho = -0.250898
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.252284
obj = -38.510696, rho = -0.191580
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.206312
obj = -45.697422, rho = -0.235609
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.171691
obj = -54.209303, rho = -0.293687
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.142991
obj = -64.128270, rho = -0.263649
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.119388
obj = -74.856586, rho = -0.197395
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -0.823710, rho = -0.961843
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.174594, rho = -0.945020
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.668338, rho = -0.920915
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -2.355844, rho = -0.886101
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.840000
obj = -3.297763, rho = -0.835692
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.840000
obj = -4.555377, rho = -0.763678
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.840000
obj = -6.163076, rho = -0.660062
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 75% (75/100) (classification)
Accuracy = 66.3% (663/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -8.059129, rho = -0.510412
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 94% (94/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760676
obj = -10.165160, rho = -0.409638
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.685137
obj = -12.639344, rho = -0.306415
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.600590
obj = -15.452261, rho = -0.313752
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.508850
obj = -18.641063, rho = -0.355580
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.425991
obj = -22.478880, rho = -0.364241
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.353440
obj = -27.117056, rho = -0.359528
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.297904
obj = -32.883663, rho = -0.367187
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.252415
obj = -39.861832, rho = -0.452827
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.216136
obj = -48.112910, rho = -0.335832
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.177492
obj = -57.155714, rho = -0.274530
nSV = 23, nBSV = 12
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.143623
obj = -69.353523, rho = -0.270318
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.121512
obj = -85.342489, rho = -0.214701
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.918021, rho = -0.926148
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.306665, rho = -0.893768
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.850889, rho = -0.847191
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.603063, rho = -0.780191
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.621576, rho = -0.683816
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.955366, rho = -0.545185
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 63% (63/100) (classification)
Accuracy = 60.8% (608/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.602300, rho = -0.345772
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 88% (88/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.888803
obj = -8.503850, rho = -0.282983
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 90% (90/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.810305
obj = -10.776998, rho = -0.221680
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.728498
obj = -13.351439, rho = -0.129273
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.626347
obj = -16.297489, rho = -0.079758
nSV = 67, nBSV = 60
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.528430
obj = -19.825965, rho = -0.069101
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.446484
obj = -24.281658, rho = -0.103884
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.371582
obj = -30.053938, rho = -0.068253
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.320402
obj = -37.463764, rho = -0.023222
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.278747
obj = -46.801899, rho = 0.050474
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.237189
obj = -58.982964, rho = 0.013170
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.205341
obj = -74.835264, rho = -0.149627
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.184111
obj = -95.458396, rho = -0.210691
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.162192
obj = -121.390236, rho = -0.221028
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.878085, rho = -0.925998
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.249260, rho = -0.893551
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.768400, rho = -0.846879
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.484581, rho = -0.779743
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.451506, rho = -0.683171
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.711474, rho = -0.544325
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 61% (61/100) (classification)
Accuracy = 54.6% (546/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.253019, rho = -0.344438
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 85% (85/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.864085
obj = -7.957560, rho = -0.196496
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 93% (93/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.761939
obj = -9.891502, rho = -0.127570
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.667382
obj = -12.125340, rho = -0.081785
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.575030
obj = -14.802470, rho = -0.058120
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.490301
obj = -17.849936, rho = 0.035763
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.417550
obj = -21.386728, rho = 0.083165
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.351179
obj = -25.252565, rho = 0.073933
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.280801
obj = -29.526098, rho = 0.052502
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.231765
obj = -34.813901, rho = 0.110721
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.188863
obj = -40.873808, rho = 0.100297
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.152338
obj = -48.239242, rho = 0.144665
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.126128
obj = -57.180116, rho = 0.142868
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.103398
obj = -68.258616, rho = 0.241350
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.863177, rho = -0.941382
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.231027, rho = -0.915681
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.748816, rho = -0.878712
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.470159, rho = -0.825533
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -3.459209, rho = -0.749038
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -4.781417, rho = -0.639004
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.475421, rho = -0.480725
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 77% (77/100) (classification)
Accuracy = 66% (660/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.481930, rho = -0.253049
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.798703
obj = -10.734355, rho = -0.209008
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.715105
obj = -13.333271, rho = -0.111455
nSV = 76, nBSV = 70
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.632884
obj = -16.294837, rho = -0.142310
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.530394
obj = -19.658304, rho = -0.136987
nSV = 57, nBSV = 49
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.455298
obj = -23.792532, rho = -0.087382
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.379947
obj = -28.515910, rho = -0.162173
nSV = 42, nBSV = 33
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.310611
obj = -34.397698, rho = -0.151315
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.256473
obj = -41.977377, rho = -0.177193
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.221088
obj = -51.590205, rho = -0.084543
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*.*
optimization finished, #iter = 166
nu = 0.191273
obj = -63.264682, rho = 0.118210
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.158706
obj = -77.221537, rho = 0.028169
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.133196
obj = -96.032756, rho = 0.049580
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.915914, rho = -0.922143
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.302305, rho = -0.888006
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.841873, rho = -0.839727
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.584406, rho = -0.769456
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.582973, rho = -0.668374
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.875492, rho = -0.522973
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 76% (76/100) (classification)
Accuracy = 74.9% (749/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.437028, rho = -0.313820
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 96% (96/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.883359
obj = -8.193442, rho = -0.214003
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.788164
obj = -10.186828, rho = -0.234327
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.683333
obj = -12.565519, rho = -0.299208
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.593258
obj = -15.290637, rho = -0.253605
nSV = 63, nBSV = 56
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.501496
obj = -18.536278, rho = -0.227616
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.422110
obj = -22.373277, rho = -0.212565
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.359247
obj = -26.775895, rho = -0.158849
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.304605
obj = -31.879759, rho = -0.222517
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.252247
obj = -37.357160, rho = -0.364722
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.204149
obj = -43.289142, rho = -0.402986
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.162874
obj = -50.589709, rho = -0.374323
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.137661
obj = -59.485411, rho = -0.551977
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.111300
obj = -67.532640, rho = -0.616631
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.876508, rho = -0.943183
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.245997, rho = -0.918272
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.761647, rho = -0.882439
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.470608, rho = -0.830894
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.422594, rho = -0.756749
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.651652, rho = -0.650096
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 61% (61/100) (classification)
Accuracy = 55.5% (555/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.129236, rho = -0.496681
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 88% (88/100) (classification)
Accuracy = 85% (850/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.705807, rho = -0.337518
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.749063
obj = -9.374003, rho = -0.311796
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.632458
obj = -11.330600, rho = -0.229668
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.533107
obj = -13.677419, rho = -0.206931
nSV = 57, nBSV = 49
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.451984
obj = -16.528605, rho = -0.180786
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.382887
obj = -19.711034, rho = -0.163429
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.316161
obj = -23.298076, rho = -0.086151
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.265011
obj = -27.375399, rho = -0.084331
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.215714
obj = -32.150694, rho = -0.086008
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.175811
obj = -37.850152, rho = -0.136033
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.148101
obj = -44.026958, rho = -0.109799
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.116427
obj = -50.540987, rho = -0.083327
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.097932
obj = -57.782555, rho = 0.036155
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -0.821630, rho = 0.929079
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.170288, rho = 0.897983
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.659428, rho = 0.853254
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -2.337401, rho = 0.788913
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -3.259599, rho = 0.696362
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -4.476405, rho = 0.563231
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -5.999671, rho = 0.371730
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 79% (79/100) (classification)
Accuracy = 71.2% (712/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.721020, rho = 0.096265
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.751077
obj = -9.552774, rho = 0.053380
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.648240
obj = -11.691155, rho = 0.089703
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.556454
obj = -14.135978, rho = 0.069971
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.468922
obj = -16.961413, rho = 0.074873
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.396818
obj = -20.259059, rho = 0.055919
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.334260
obj = -23.718107, rho = 0.109417
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.269110
obj = -27.687519, rho = 0.153930
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.219687
obj = -32.227419, rho = 0.230296
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.184183
obj = -36.849536, rho = 0.195450
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.140586
obj = -41.583807, rho = 0.195882
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.111635
obj = -47.860886, rho = 0.171585
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
....*
optimization finished, #iter = 426
nu = 0.086822
obj = -55.267969, rho = 0.190243
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.932656, rho = 0.883544
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.324334, rho = 0.832484
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.869305, rho = 0.759036
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.615067, rho = 0.653386
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.608872, rho = 0.501412
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.875076, rho = 0.282807
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 74% (74/100) (classification)
Accuracy = 70.8% (708/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.358487, rho = -0.005599
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 99% (99/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.868481
obj = -8.030845, rho = -0.070340
nSV = 90, nBSV = 84
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.768915
obj = -10.006352, rho = -0.097133
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.662833
obj = -12.431517, rho = -0.122732
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.583215
obj = -15.223303, rho = -0.142831
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.500000
obj = -18.426315, rho = -0.106049
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.412222
obj = -22.282791, rho = -0.100404
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.347239
obj = -27.296343, rho = -0.083245
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.299731
obj = -33.184023, rho = -0.122209
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.260974
obj = -39.976906, rho = -0.051578
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.211972
obj = -47.758566, rho = -0.100101
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.178698
obj = -56.912376, rho = -0.036914
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.146906
obj = -67.967195, rho = -0.067770
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.119692
obj = -81.808794, rho = -0.099642
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.932946, rho = -0.900162
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.324933, rho = -0.856388
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.870545, rho = -0.793421
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.617633, rho = -0.702846
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.614181, rho = -0.572559
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 56% (56/100) (classification)
Accuracy = 53.1% (531/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.886061, rho = -0.385147
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 85% (85/100) (classification)
Accuracy = 83.6% (836/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.398347, rho = -0.242440
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.170748, rho = -0.219514
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -10.285784, rho = -0.157817
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.686989
obj = -12.713227, rho = -0.063397
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.594487
obj = -15.600800, rho = -0.090195
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.513266
obj = -19.024245, rho = -0.192202
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.432549
obj = -22.996396, rho = -0.239050
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.354937
obj = -27.916692, rho = -0.241246
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.296652
obj = -34.490412, rho = -0.219542
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.263222
obj = -42.849235, rho = -0.265204
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.228160
obj = -51.815795, rho = -0.280417
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.193502
obj = -62.293804, rho = -0.279022
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.158283
obj = -75.314857, rho = -0.281864
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*..*
optimization finished, #iter = 377
nu = 0.133143
obj = -91.201439, rho = -0.374078
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -0.825996, rho = -0.954935
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -1.179321, rho = -0.935176
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -1.678119, rho = -0.906754
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -2.376077, rho = -0.865870
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -3.339625, rho = -0.807061
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -4.641989, rho = -0.722466
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -6.342288, rho = -0.600782
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 61% (61/100) (classification)
Accuracy = 55.3% (553/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -8.429942, rho = -0.425745
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 87% (87/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -10.719869, rho = -0.247509
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.720000
obj = -13.217794, rho = -0.173018
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.615282
obj = -16.229028, rho = -0.117304
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.531147
obj = -19.777223, rho = -0.051323
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.445649
obj = -24.080829, rho = -0.048088
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.379110
obj = -29.444514, rho = -0.070853
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.326799
obj = -35.711631, rho = -0.077522
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.274607
obj = -42.721378, rho = -0.113178
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.228729
obj = -51.448325, rho = -0.120824
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.193008
obj = -61.716755, rho = -0.206413
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.165225
obj = -73.037317, rho = -0.135509
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.141169
obj = -84.285557, rho = -0.170292
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -0.840800, rho = 0.928512
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.197339, rho = 0.897168
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.697257, rho = 0.852081
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -2.389575, rho = 0.787538
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -3.330011, rho = 0.694384
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -4.568093, rho = 0.560387
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 60% (60/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -6.111712, rho = 0.367077
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 80% (80/100) (classification)
Accuracy = 70.9% (709/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.825451
obj = -7.863844, rho = 0.163221
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 91% (91/100) (classification)
Accuracy = 91.5% (915/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.756514
obj = -9.910321, rho = -0.001135
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.676482
obj = -12.174851, rho = -0.036288
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.568258
obj = -14.850068, rho = -0.020863
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.478583
obj = -18.094400, rho = -0.047565
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.412349
obj = -22.078747, rho = 0.013842
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.343842
obj = -26.908692, rho = 0.087660
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.289408
obj = -32.856379, rho = 0.103475
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.243328
obj = -40.540976, rho = 0.113686
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.205884
obj = -50.474298, rho = 0.187667
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.183965
obj = -63.321484, rho = 0.175403
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.162240
obj = -78.120557, rho = 0.138790
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.139150
obj = -95.469234, rho = 0.064244
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.963958, rho = -0.040596
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.363874, rho = -0.058395
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.914831, rho = -0.083999
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.657068, rho = -0.120828
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.620690, rho = -0.173805
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.991142
obj = -4.792211, rho = -0.243044
nSV = 100, nBSV = 98
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.926253
obj = -6.155596, rho = -0.287493
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.837646
obj = -7.805853, rho = -0.218088
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.734791
obj = -9.754554, rho = -0.165826
nSV = 76, nBSV = 71
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.644020
obj = -12.191325, rho = -0.265547
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.558544
obj = -15.167087, rho = -0.240181
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.486158
obj = -18.856232, rho = -0.169287
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.423971
obj = -23.276440, rho = -0.215243
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.362877
obj = -28.467507, rho = -0.256051
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.309487
obj = -34.835773, rho = -0.212928
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.270912
obj = -42.481525, rho = -0.182782
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.228154
obj = -50.901904, rho = -0.270875
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.188669
obj = -61.598935, rho = -0.359379
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.160202
obj = -73.707640, rho = -0.324859
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.131270
obj = -88.322047, rho = -0.378287
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.947594, rho = -0.883936
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.342628, rho = -0.833047
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.889013, rho = -0.759847
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.629748, rho = -0.654552
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.601705, rho = -0.503091
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 59% (59/100) (classification)
Accuracy = 57.6% (576/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.806244, rho = -0.285221
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 90% (90/100) (classification)
Accuracy = 89.3% (893/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.938803
obj = -6.173575, rho = -0.244564
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.853248
obj = -7.746784, rho = -0.167319
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.755039
obj = -9.529320, rho = -0.113665
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.643767
obj = -11.633298, rho = -0.076907
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.558665
obj = -13.954920, rho = -0.047471
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.468594
obj = -16.504575, rho = 0.006219
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.383315
obj = -19.471225, rho = -0.009416
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.316460
obj = -23.063056, rho = 0.066985
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.262031
obj = -27.053444, rho = 0.125798
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.212137
obj = -31.519943, rho = 0.110416
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.171233
obj = -37.019061, rho = 0.123222
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.139608
obj = -43.916281, rho = 0.090119
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.123807
obj = -50.108495, rho = -0.012275
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.097837
obj = -54.517223, rho = 0.001171
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -0.764853, rho = 0.938555
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.780000
obj = -1.090650, rho = 0.911744
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -1.549078, rho = 0.873048
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.780000
obj = -2.187377, rho = 0.817641
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.780000
obj = -3.061812, rho = 0.737818
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -4.229176, rho = 0.623543
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.780000
obj = -5.721163, rho = 0.458167
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 70% (70/100) (classification)
Accuracy = 58.9% (589/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.780000
obj = -7.479974, rho = 0.220734
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 91% (91/100) (classification)
Accuracy = 86.5% (865/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.736966
obj = -9.357791, rho = 0.004356
nSV = 76, nBSV = 71
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.644594
obj = -11.387645, rho = 0.062304
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.541642
obj = -13.656281, rho = 0.081064
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.459366
obj = -16.287165, rho = 0.043660
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.375830
obj = -19.337061, rho = 0.019077
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.316821
obj = -22.871641, rho = -0.022757
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.260845
obj = -26.668349, rho = -0.038600
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.212291
obj = -30.881967, rho = -0.061487
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.168457
obj = -36.002047, rho = -0.141774
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.136781
obj = -41.912039, rho = -0.150868
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 152
nu = 0.110660
obj = -48.995571, rho = -0.168959
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 323
nu = 0.091018
obj = -56.944687, rho = -0.242054
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.861537, rho = -0.937984
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.227634, rho = -0.910793
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.741797, rho = -0.871680
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -2.455634, rho = -0.815417
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.429155, rho = -0.734487
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.719231, rho = -0.618073
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 58% (58/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.346749, rho = -0.450617
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 80% (80/100) (classification)
Accuracy = 77.2% (772/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -8.227772, rho = -0.256046
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 95% (95/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.786530
obj = -10.356719, rho = -0.206479
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.688492
obj = -12.856316, rho = -0.155841
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.585525
obj = -15.940140, rho = -0.129178
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.500492
obj = -20.040272, rho = -0.084528
nSV = 52, nBSV = 50
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.448883
obj = -25.081528, rho = -0.208687
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.386141
obj = -31.303059, rho = -0.200857
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.341780
obj = -38.647618, rho = -0.074962
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.295519
obj = -47.002741, rho = -0.143306
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.245602
obj = -57.462113, rho = -0.197793
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.215434
obj = -70.590926, rho = -0.208052
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 181
nu = 0.185285
obj = -84.618654, rho = -0.212166
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.151742
obj = -101.547345, rho = -0.286608
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.952762, rho = -0.903911
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.353322, rho = -0.861781
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.911141, rho = -0.801178
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.675534, rho = -0.714005
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.696442, rho = -0.588610
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.002266, rho = -0.408237
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 81% (81/100) (classification)
Accuracy = 76.9% (769/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.962611
obj = -6.547559, rho = -0.227208
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 96% (96/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.901590
obj = -8.327254, rho = -0.185704
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.801732
obj = -10.295377, rho = -0.145320
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.680098
obj = -12.682010, rho = -0.132910
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.596917
obj = -15.608633, rho = -0.187298
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.506921
obj = -18.998129, rho = -0.229270
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.436978
obj = -23.037010, rho = -0.177596
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.367261
obj = -27.720290, rho = -0.158730
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.307603
obj = -33.190265, rho = -0.164453
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.255629
obj = -39.640758, rho = -0.206579
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.208413
obj = -47.714856, rho = -0.240966
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.172047
obj = -57.899710, rho = -0.279123
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
...*.*
optimization finished, #iter = 421
nu = 0.144893
obj = -71.587164, rho = -0.313647
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*..*
optimization finished, #iter = 452
nu = 0.125016
obj = -89.226343, rho = -0.459926
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.914085, rho = -0.935800
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.298521, rho = -0.907652
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.834038, rho = -0.867162
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.568197, rho = -0.808919
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.549432, rho = -0.725140
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.806091, rho = -0.604628
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 76% (76/100) (classification)
Accuracy = 72.2% (722/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.933130
obj = -6.294051, rho = -0.446552
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 91% (91/100) (classification)
Accuracy = 92.2% (922/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.852895
obj = -8.014083, rho = -0.427064
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 93% (93/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.759852
obj = -10.107360, rho = -0.400417
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 95% (95/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.667099
obj = -12.636401, rho = -0.352269
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.590552
obj = -15.652594, rho = -0.299093
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.513752
obj = -19.149009, rho = -0.342639
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.434871
obj = -23.240073, rho = -0.347948
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.371186
obj = -28.063824, rho = -0.326882
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.319286
obj = -33.330115, rho = -0.339502
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.256582
obj = -39.321327, rho = -0.338237
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.219921
obj = -46.458255, rho = -0.402271
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.173832
obj = -54.386092, rho = -0.409111
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 193
nu = 0.141045
obj = -63.966471, rho = -0.406605
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 401
nu = 0.117032
obj = -75.939119, rho = -0.429275
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949978, rho = 0.838755
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.347561, rho = 0.768057
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.899225, rho = 0.665949
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.650877, rho = 0.519484
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.645424, rho = 0.308802
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 60% (60/100) (classification)
Accuracy = 56% (560/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.896703, rho = 0.005746
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 84% (84/100) (classification)
Accuracy = 80.7% (807/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.951946
obj = -6.339382, rho = -0.267752
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.857681
obj = -8.016717, rho = -0.258019
nSV = 91, nBSV = 83
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.778383
obj = -10.017508, rho = -0.191544
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.681436
obj = -12.225272, rho = -0.172334
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.580000
obj = -14.754318, rho = -0.196995
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.481497
obj = -17.784973, rho = -0.180917
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.405882
obj = -21.469556, rho = -0.149793
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.337293
obj = -25.873788, rho = -0.180687
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.281659
obj = -31.329465, rho = -0.169772
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.239828
obj = -38.061095, rho = -0.238454
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.198563
obj = -46.072563, rho = -0.266949
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.167637
obj = -56.389732, rho = -0.261462
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.142625
obj = -69.430889, rho = -0.351751
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.123958
obj = -85.439964, rho = -0.445461
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949420, rho = 0.833878
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.346406, rho = 0.761041
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.896831, rho = 0.656270
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.645923, rho = 0.505561
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.635173, rho = 0.288775
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 61% (61/100) (classification)
Accuracy = 62% (620/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.875494, rho = -0.023062
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 86% (86/100) (classification)
Accuracy = 86.5% (865/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.935511
obj = -6.313500, rho = -0.187288
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.855472
obj = -8.006987, rho = -0.205753
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.758661
obj = -10.067099, rho = -0.278581
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.667637
obj = -12.540135, rho = -0.208200
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.569728
obj = -15.563814, rho = -0.199165
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.495280
obj = -19.315122, rho = -0.271366
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.428367
obj = -24.115598, rho = -0.296478
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.370422
obj = -30.032032, rho = -0.367956
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.323106
obj = -37.540148, rho = -0.356257
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.283625
obj = -46.626750, rho = -0.553661
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.241684
obj = -57.501098, rho = -0.565040
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.208427
obj = -71.216721, rho = -0.611153
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.185195
obj = -87.332558, rho = -0.653674
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.156416
obj = -105.861995, rho = -0.640303
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -0.824781, rho = -0.960455
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -1.176808, rho = -0.943117
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -1.672920, rho = -0.918176
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -2.365318, rho = -0.882301
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -3.317363, rho = -0.830696
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -4.595926, rho = -0.756464
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -6.246976, rho = -0.649686
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 68% (68/100) (classification)
Accuracy = 57.9% (579/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -8.232728, rho = -0.496091
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 87% (87/100) (classification)
Accuracy = 85.3% (853/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.780000
obj = -10.457424, rho = -0.381374
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.695100
obj = -12.951400, rho = -0.320657
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.604125
obj = -16.035881, rho = -0.240774
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.521974
obj = -19.644929, rho = -0.133097
nSV = 56, nBSV = 48
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.436532
obj = -24.163992, rho = -0.114830
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.389706
obj = -29.693647, rho = -0.090070
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.325509
obj = -35.921466, rho = -0.096161
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.275227
obj = -43.279575, rho = -0.157589
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.239219
obj = -51.831339, rho = -0.116696
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.195368
obj = -61.402261, rho = -0.139811
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.163536
obj = -72.114698, rho = -0.150782
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.137020
obj = -81.646684, rho = -0.186674
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.897591, rho = 0.894151
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.277007, rho = 0.847741
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -1.807668, rho = 0.780983
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.539732, rho = 0.684955
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.528079, rho = 0.546824
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.815912, rho = 0.348129
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 66% (66/100) (classification)
Accuracy = 65% (650/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.391431, rho = 0.062316
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 86% (86/100) (classification)
Accuracy = 88.3% (883/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.193664, rho = -0.149786
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.777099
obj = -10.243124, rho = -0.138065
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.685599
obj = -12.743201, rho = -0.099374
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.585518
obj = -15.741101, rho = -0.118512
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.518189
obj = -19.337412, rho = -0.073751
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.436473
obj = -23.511472, rho = -0.081657
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.365481
obj = -28.771516, rho = -0.099806
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.314823
obj = -35.257216, rho = -0.106023
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.269663
obj = -43.050824, rho = -0.161022
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.228351
obj = -52.165469, rho = -0.205494
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.193488
obj = -63.082074, rho = -0.202248
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.161627
obj = -76.544543, rho = -0.174844
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.138640
obj = -91.438130, rho = -0.248106
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.878600, rho = -0.926221
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.250324, rho = -0.893873
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.770602, rho = -0.847341
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.489137, rho = -0.780408
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.460933, rho = -0.684127
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.730981, rho = -0.545633
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 64% (64/100) (classification)
Accuracy = 55.4% (554/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.293379, rho = -0.346416
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 91% (91/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.864847
obj = -8.040077, rho = -0.142932
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.769223
obj = -10.062265, rho = -0.073821
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.690134
obj = -12.336515, rho = -0.123260
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.592786
obj = -14.849392, rho = -0.098046
nSV = 60, nBSV = 58
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.500000
obj = -17.572058, rho = -0.080309
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.412297
obj = -20.499104, rho = -0.027302
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.338770
obj = -23.766808, rho = -0.049870
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.271221
obj = -27.314548, rho = -0.014350
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.214008
obj = -31.504592, rho = 0.000926
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.174837
obj = -36.567031, rho = 0.082775
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.144444
obj = -41.774285, rho = 0.141076
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.115686
obj = -46.594574, rho = 0.040747
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.087481
obj = -51.698744, rho = 0.042116
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.916004, rho = -0.929098
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.302492, rho = -0.898010
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.842255, rho = -0.853293
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.585198, rho = -0.788969
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -3.584611, rho = -0.696443
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -4.878881, rho = -0.563348
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 59% (59/100) (classification)
Accuracy = 56.1% (561/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -6.444042, rho = -0.371898
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 89% (89/100) (classification)
Accuracy = 89.1% (891/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.897203
obj = -8.194425, rho = -0.179893
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.797730
obj = -10.165818, rho = -0.120707
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.688308
obj = -12.421418, rho = -0.045778
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.591766
obj = -15.045548, rho = -0.014693
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.498360
obj = -18.027967, rho = 0.039630
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.409726
obj = -21.672469, rho = -0.016525
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.339510
obj = -26.066367, rho = 0.022236
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.295976
obj = -31.531373, rho = -0.119262
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.253328
obj = -37.178633, rho = -0.125058
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.201506
obj = -43.375069, rho = -0.128686
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.165577
obj = -51.170764, rho = -0.184590
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.140311
obj = -58.996116, rho = -0.242816
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.112810
obj = -65.760045, rho = -0.229437
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.933124, rho = 0.858047
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.325302, rho = 0.795807
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.871308, rho = 0.706279
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.619213, rho = 0.577497
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.617449, rho = 0.392250
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.892823, rho = 0.125783
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 83% (83/100) (classification)
Accuracy = 75.7% (757/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.395207, rho = -0.243541
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.871087
obj = -8.089050, rho = -0.208014
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.778944
obj = -10.075685, rho = -0.220465
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.671089
obj = -12.396379, rho = -0.160456
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.573794
obj = -15.256193, rho = -0.095688
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.500000
obj = -18.733468, rho = -0.144127
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.422877
obj = -22.837308, rho = -0.134340
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.364072
obj = -27.670159, rho = -0.074092
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.307489
obj = -33.235861, rho = -0.039862
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.259875
obj = -39.596217, rho = -0.030473
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.215772
obj = -46.818080, rho = 0.027589
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.175243
obj = -55.483904, rho = -0.023486
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.144612
obj = -66.216708, rho = -0.070580
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.121963
obj = -78.385640, rho = -0.061605
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.931922, rho = -0.918150
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.322815, rho = -0.882263
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.866163, rho = -0.830641
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.608567, rho = -0.756385
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.595421, rho = -0.649572
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 57% (57/100) (classification)
Accuracy = 52.4% (524/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.847244, rho = -0.495927
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 79% (79/100) (classification)
Accuracy = 78.9% (789/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.929288
obj = -6.316209, rho = -0.345958
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 95% (95/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.859852
obj = -8.059301, rho = -0.292912
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.772708
obj = -10.114651, rho = -0.209690
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.672528
obj = -12.559212, rho = -0.147958
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.598070
obj = -15.490376, rho = -0.242388
nSV = 60, nBSV = 58
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.508534
obj = -18.795638, rho = -0.193423
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.424086
obj = -22.842220, rho = -0.230559
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.361470
obj = -27.758914, rho = -0.180452
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.306774
obj = -33.537910, rho = -0.157415
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.260000
obj = -40.639941, rho = -0.231216
nSV = 27, nBSV = 24
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.213534
obj = -48.937422, rho = -0.242657
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.179180
obj = -59.531675, rho = -0.207326
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.153970
obj = -72.424072, rho = -0.169281
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.133106
obj = -87.066374, rho = -0.223343
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -0.803153, rho = 0.931118
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.820000
obj = -1.144676, rho = 0.899402
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.820000
obj = -1.624579, rho = 0.856153
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.820000
obj = -2.291394, rho = 0.793083
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.820000
obj = -3.201947, rho = 0.702361
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -4.411118, rho = 0.571994
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -5.942265, rho = 0.384335
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 72% (72/100) (classification)
Accuracy = 67.2% (672/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -7.713979, rho = 0.114397
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 95% (95/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.749245
obj = -9.644828, rho = -0.025939
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.664768
obj = -11.759693, rho = -0.078713
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.565899
obj = -14.138074, rho = -0.114614
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.463365
obj = -16.930080, rho = -0.074999
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.398383
obj = -20.262935, rho = -0.102009
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.329966
obj = -23.620781, rho = -0.215085
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.266986
obj = -27.782529, rho = -0.186735
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.221625
obj = -32.003019, rho = -0.290336
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.178208
obj = -36.471555, rho = -0.346579
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.142135
obj = -41.405800, rho = -0.504203
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 219
nu = 0.117012
obj = -46.254762, rho = -0.433599
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 297
nu = 0.088446
obj = -50.569373, rho = -0.426082
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.916717, rho = -0.938845
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.303968, rho = -0.912032
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.845310, rho = -0.873462
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.591518, rho = -0.817981
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.597688, rho = -0.738175
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.905939, rho = -0.623378
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 57% (57/100) (classification)
Accuracy = 53.5% (535/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.500027, rho = -0.458248
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 85% (85/100) (classification)
Accuracy = 84.7% (847/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.913458
obj = -8.227631, rho = -0.277524
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.786818
obj = -10.179549, rho = -0.268859
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.693918
obj = -12.430848, rho = -0.207962
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.585851
obj = -14.934264, rho = -0.208745
nSV = 63, nBSV = 56
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.493813
obj = -17.950074, rho = -0.188819
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.411937
obj = -21.468802, rho = -0.172639
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.350374
obj = -25.357691, rho = -0.065839
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.282480
obj = -29.905592, rho = -0.108533
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.241048
obj = -35.260614, rho = -0.233229
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.190376
obj = -41.050987, rho = -0.217199
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.157638
obj = -48.143373, rho = -0.276897
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.125668
obj = -56.301602, rho = -0.245759
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 196
nu = 0.106226
obj = -65.103092, rho = -0.164522
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -0.841589, rho = 0.916463
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.198972, rho = 0.879836
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.700635, rho = 0.827150
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -2.396564, rho = 0.751364
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -3.344474, rho = 0.642350
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -4.598018, rho = 0.485539
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.173623, rho = 0.259973
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 81% (81/100) (classification)
Accuracy = 73.3% (733/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -7.997549, rho = 0.016987
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 92% (92/100) (classification)
Accuracy = 90.8% (908/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.750110
obj = -10.106457, rho = 0.021460
nSV = 77, nBSV = 72
Total nSV = 77
Accuracy = 95% (95/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.680000
obj = -12.615931, rho = -0.018693
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.593814
obj = -15.451490, rho = -0.034362
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.517493
obj = -18.758767, rho = 0.006357
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.435660
obj = -22.442028, rho = 0.070756
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.358535
obj = -26.751275, rho = 0.029778
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.299568
obj = -32.001963, rho = 0.006947
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.244240
obj = -38.010720, rho = -0.043988
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.204362
obj = -45.794757, rho = -0.136207
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.174533
obj = -54.815155, rho = -0.221067
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.149364
obj = -63.092878, rho = -0.229995
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*.*
optimization finished, #iter = 339
nu = 0.116429
obj = -71.938449, rho = -0.248006
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -0.876911, rho = -0.944150
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.246830, rho = -0.919663
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.763372, rho = -0.884439
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -2.474183, rho = -0.833480
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -3.429992, rho = -0.760469
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -4.666959, rho = -0.655447
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 64% (64/100) (classification)
Accuracy = 61.2% (612/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.895308
obj = -6.161192, rho = -0.510950
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 84.9% (849/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.856958
obj = -7.864582, rho = -0.378957
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.773863
obj = -9.696235, rho = -0.333220
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.661913
obj = -11.720629, rho = -0.344005
nSV = 70, nBSV = 61
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.547245
obj = -14.210477, rho = -0.382610
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.464827
obj = -17.196771, rho = -0.403170
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.404150
obj = -20.653221, rho = -0.495803
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.329339
obj = -24.525158, rho = -0.488350
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.274183
obj = -29.176992, rho = -0.463906
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.223921
obj = -34.661602, rho = -0.517143
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.182128
obj = -41.901254, rho = -0.602027
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.152236
obj = -50.851379, rho = -0.734402
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.125174
obj = -62.981827, rho = -0.697685
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.106997
obj = -79.195959, rho = -0.747673
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.932752, rho = -0.923920
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.324533, rho = -0.890562
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.869716, rho = -0.842579
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.615919, rho = -0.773558
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.610633, rho = -0.674274
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.878721, rho = -0.531460
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 71% (71/100) (classification)
Accuracy = 72.6% (726/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.954079
obj = -6.366587, rho = -0.346604
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.048292, rho = -0.310846
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.801008
obj = -9.828357, rho = -0.205225
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.687337
obj = -11.659417, rho = -0.152166
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.563049
obj = -13.619966, rho = -0.183723
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.458083
obj = -15.907231, rho = -0.168846
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.372705
obj = -18.579312, rho = -0.226901
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.301988
obj = -21.793093, rho = -0.211561
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.246292
obj = -25.415719, rho = -0.287092
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.197136
obj = -29.716579, rho = -0.320344
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.170524
obj = -34.562423, rho = -0.220832
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.135695
obj = -38.683769, rho = -0.198808
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.108287
obj = -42.963842, rho = -0.126046
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.........*
optimization finished, #iter = 990
nu = 0.080796
obj = -47.201650, rho = -0.145913
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.916724, rho = -0.932278
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.303983, rho = -0.902585
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.845339, rho = -0.859873
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.591580, rho = -0.798435
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.597816, rho = -0.710059
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.906203, rho = -0.582934
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 68% (68/100) (classification)
Accuracy = 64.9% (649/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.500574, rho = -0.400071
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 91% (91/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.881317
obj = -8.300460, rho = -0.256581
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.793828
obj = -10.431609, rho = -0.239505
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.713512
obj = -12.832198, rho = -0.125622
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.596658
obj = -15.593140, rho = -0.147307
nSV = 65, nBSV = 56
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.505179
obj = -19.087187, rho = -0.174578
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.430307
obj = -23.306853, rho = -0.104317
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.371981
obj = -28.248239, rho = -0.217098
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.310015
obj = -33.958150, rho = -0.212949
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.259441
obj = -40.905733, rho = -0.288017
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.218879
obj = -49.653597, rho = -0.260576
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.179089
obj = -60.239241, rho = -0.294830
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.154969
obj = -73.886838, rho = -0.179751
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 207
nu = 0.132959
obj = -89.503030, rho = -0.220301
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.924403, rho = 0.822078
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.307256, rho = 0.744067
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.833968, rho = 0.631854
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.541952, rho = 0.470440
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.457585, rho = 0.238255
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 73% (73/100) (classification)
Accuracy = 65.8% (658/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.562043, rho = -0.095732
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 92% (92/100) (classification)
Accuracy = 86.9% (869/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.893674
obj = -5.799567, rho = -0.224828
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.794384
obj = -7.241592, rho = -0.240022
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.698772
obj = -8.977680, rho = -0.223255
nSV = 70, nBSV = 68
Total nSV = 70
Accuracy = 95% (95/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.612065
obj = -10.962845, rho = -0.154826
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.521709
obj = -13.257351, rho = -0.170835
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.435792
obj = -15.941234, rho = -0.142699
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.362159
obj = -19.236044, rho = -0.138505
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.301839
obj = -23.245968, rho = -0.143603
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.255154
obj = -28.179495, rho = -0.034679
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.210991
obj = -34.448081, rho = -0.035524
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.186909
obj = -41.987177, rho = 0.021353
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.157318
obj = -49.751989, rho = 0.045496
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.130806
obj = -59.355280, rho = 0.076115
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.106283
obj = -71.103436, rho = 0.010580
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.890917, rho = -0.917767
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.263197, rho = -0.881711
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.779094, rho = -0.829848
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.480608, rho = -0.755245
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.405742, rho = -0.647932
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.562780, rho = -0.493567
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 84% (84/100) (classification)
Accuracy = 74.7% (747/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -5.873430, rho = -0.335562
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.809595
obj = -7.360765, rho = -0.318776
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.720000
obj = -9.086276, rho = -0.243570
nSV = 72, nBSV = 72
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.619475
obj = -10.969877, rho = -0.190313
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.514985
obj = -13.208074, rho = -0.197625
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.431702
obj = -15.919262, rho = -0.220418
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.361298
obj = -19.208865, rho = -0.208291
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.301800
obj = -23.152806, rho = -0.155407
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.249327
obj = -28.275122, rho = -0.149239
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.216705
obj = -34.365190, rho = -0.182042
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.178782
obj = -41.907253, rho = -0.207235
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.151835
obj = -51.596164, rho = -0.264571
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.134903
obj = -62.713238, rho = -0.261853
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.116848
obj = -74.526807, rho = -0.004594
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.924745, rho = -0.915389
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.307964, rho = -0.878292
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.835434, rho = -0.824167
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.544985, rho = -0.747072
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -3.463863, rho = -0.636176
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 76% (76/100) (classification)
Accuracy = 68.3% (683/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -4.575032, rho = -0.476658
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 90% (90/100) (classification)
Accuracy = 86.7% (867/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.899459
obj = -5.804326, rho = -0.385248
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 94% (94/100) (classification)
Accuracy = 89.7% (897/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -7.261819, rho = -0.389884
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.696018
obj = -8.975057, rho = -0.429404
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.606703
obj = -11.029780, rho = -0.415684
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.520295
obj = -13.409361, rho = -0.379609
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.434392
obj = -16.314684, rho = -0.341748
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.370475
obj = -19.973993, rho = -0.333608
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.314815
obj = -24.259485, rho = -0.351372
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.265779
obj = -29.395052, rho = -0.376443
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.223905
obj = -35.436498, rho = -0.330726
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.195730
obj = -42.197056, rho = -0.190025
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.162331
obj = -49.590221, rho = -0.155583
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.129253
obj = -57.998572, rho = -0.177943
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*..*
optimization finished, #iter = 267
nu = 0.106173
obj = -67.917981, rho = -0.136408
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.971154, rho = -0.020068
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.378763, rho = -0.028867
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.945638, rho = -0.041523
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.720814, rho = -0.059729
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.752589, rho = -0.085917
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.064440, rho = -0.123587
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.979672
obj = -6.607896, rho = -0.200142
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.897039
obj = -8.438004, rho = -0.195350
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.806762
obj = -10.578506, rho = -0.222351
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.713131
obj = -13.033492, rho = -0.185336
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.612013
obj = -15.971212, rho = -0.121301
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.525353
obj = -19.345068, rho = -0.107791
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.436773
obj = -23.313703, rho = -0.130816
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.367627
obj = -28.296625, rho = -0.104159
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.307138
obj = -34.501270, rho = -0.084833
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.258011
obj = -42.499976, rho = -0.095740
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.227685
obj = -52.032564, rho = 0.051466
nSV = 25, nBSV = 21
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.195940
obj = -62.441782, rho = -0.084761
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.161152
obj = -75.140291, rho = -0.042842
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.136186
obj = -89.887473, rho = -0.084504
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.929484, rho = -0.920423
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.317771, rho = -0.885532
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.855725, rho = -0.835343
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.586969, rho = -0.763150
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.550732, rho = -0.659303
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 58% (58/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.754776, rho = -0.509924
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 86% (86/100) (classification)
Accuracy = 78.7% (787/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.932753
obj = -6.132873, rho = -0.377372
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.824562
obj = -7.772580, rho = -0.316600
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.744808
obj = -9.722499, rho = -0.270639
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.664328
obj = -11.878643, rho = -0.310585
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.560917
obj = -14.404323, rho = -0.289814
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.477556
obj = -17.294468, rho = -0.247371
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.398850
obj = -20.683959, rho = -0.195967
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.324743
obj = -24.868815, rho = -0.174253
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.277418
obj = -29.923710, rho = -0.192900
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.231667
obj = -35.830401, rho = -0.220549
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.190689
obj = -42.783051, rho = -0.245725
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.160119
obj = -51.429138, rho = -0.467643
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.135268
obj = -61.672989, rho = -0.468891
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.110891
obj = -73.874383, rho = -0.494908
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.900087, rho = -0.935044
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.282174, rho = -0.906551
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.818358, rho = -0.865578
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -2.561854, rho = -0.806724
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -3.573852, rho = -0.721982
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -4.910623, rho = -0.600143
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 61% (61/100) (classification)
Accuracy = 58.2% (582/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -6.587407, rho = -0.424661
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 84% (84/100) (classification)
Accuracy = 86.1% (861/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.884921
obj = -8.530480, rho = -0.245925
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.795340
obj = -10.838272, rho = -0.166889
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.721079
obj = -13.631186, rho = -0.219757
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.640303
obj = -16.807598, rho = -0.089587
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.545726
obj = -20.587542, rho = -0.084207
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.465572
obj = -25.180333, rho = -0.062294
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.392764
obj = -30.933194, rho = -0.103823
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.336152
obj = -37.967955, rho = -0.082371
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.288789
obj = -46.389540, rho = 0.040861
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.246532
obj = -56.235145, rho = 0.017247
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.202029
obj = -68.752757, rho = -0.004723
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.176008
obj = -84.455068, rho = -0.047588
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.150938
obj = -103.092821, rho = 0.011824
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -0.804793, rho = 0.933575
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -1.148064, rho = 0.904334
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -1.631588, rho = 0.862389
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -2.305897, rho = 0.802053
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -3.231956, rho = 0.715263
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -4.473212, rho = 0.590420
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -6.070745, rho = 0.410840
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 66% (66/100) (classification)
Accuracy = 58.6% (586/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -7.979823, rho = 0.152523
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 85% (85/100) (classification)
Accuracy = 90.8% (908/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.766119
obj = -10.119108, rho = 0.002967
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.685447
obj = -12.472837, rho = -0.093407
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.589168
obj = -15.187050, rho = -0.108275
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.504590
obj = -18.358300, rho = -0.157496
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.426756
obj = -21.872399, rho = -0.168201
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.355570
obj = -25.686129, rho = -0.272328
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.293519
obj = -29.701055, rho = -0.320001
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.236526
obj = -34.137415, rho = -0.271753
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.188016
obj = -39.310551, rho = -0.336498
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.148414
obj = -45.339903, rho = -0.401749
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.120968
obj = -52.897750, rho = -0.501515
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 152
nu = 0.098011
obj = -60.619834, rho = -0.611950
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -0.839092, rho = -0.940046
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.193806, rho = -0.913760
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.689945, rho = -0.875948
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.374447, rho = -0.821557
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.298710, rho = -0.743319
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.503326, rho = -0.630777
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 69% (69/100) (classification)
Accuracy = 57% (570/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -5.977693, rho = -0.468891
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 92% (92/100) (classification)
Accuracy = 83.7% (837/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.630391, rho = -0.346609
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.730220
obj = -9.455681, rho = -0.315844
nSV = 76, nBSV = 71
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.637308
obj = -11.628403, rho = -0.341768
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.551170
obj = -14.062840, rho = -0.287023
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.459412
obj = -17.059843, rho = -0.300889
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.396192
obj = -20.615963, rho = -0.443115
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.328320
obj = -24.660005, rho = -0.452417
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.283391
obj = -29.118390, rho = -0.451926
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.230870
obj = -34.060479, rho = -0.408209
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.188618
obj = -39.473198, rho = -0.529644
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.149256
obj = -45.576548, rho = -0.604750
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.119498
obj = -53.192509, rho = -0.593777
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.096359
obj = -62.849369, rho = -0.552370
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.952142, rho = 0.864919
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.352038, rho = 0.805693
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.908484, rho = 0.720500
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.670035, rho = 0.597953
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.685065, rho = 0.421675
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.978726, rho = 0.168109
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 78% (78/100) (classification)
Accuracy = 77.8% (778/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.970924
obj = -6.496409, rho = -0.137726
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 95% (95/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.895878
obj = -8.217919, rho = -0.147579
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.790531
obj = -10.185626, rho = -0.105435
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.680000
obj = -12.549045, rho = -0.139333
nSV = 69, nBSV = 67
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.597070
obj = -15.300709, rho = -0.177599
nSV = 60, nBSV = 58
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.496793
obj = -18.483287, rho = -0.174840
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.417617
obj = -22.609284, rho = -0.149198
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.354728
obj = -27.679816, rho = -0.141940
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.305012
obj = -33.927914, rho = -0.126892
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.261967
obj = -41.139107, rho = -0.033437
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.221396
obj = -49.376943, rho = 0.139005
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.186909
obj = -58.292659, rho = 0.142335
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.154156
obj = -68.735275, rho = -0.003346
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.125426
obj = -81.604563, rho = 0.083827
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.968193, rho = -0.046585
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.372636, rho = -0.067009
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.932961, rho = -0.096390
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.694582, rho = -0.138652
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.698313, rho = -0.199444
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.952135, rho = -0.286890
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.953025
obj = -6.453260, rho = -0.230878
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.234778, rho = -0.266849
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.806413
obj = -10.199261, rho = -0.217802
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.699492
obj = -12.360321, rho = -0.156522
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.588588
obj = -14.766681, rho = -0.101716
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.493725
obj = -17.545417, rho = -0.168954
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.407636
obj = -20.755797, rho = -0.130280
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.334249
obj = -24.436445, rho = -0.167507
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.274175
obj = -28.915405, rho = -0.220077
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.222583
obj = -34.292824, rho = -0.214106
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.186258
obj = -40.982274, rho = -0.139716
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.165403
obj = -47.181348, rho = -0.040128
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.130266
obj = -52.791952, rho = -0.036991
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.098984
obj = -59.171298, rho = -0.041716
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.897209, rho = -0.938199
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.276217, rho = -0.911102
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.806033, rho = -0.872124
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.536349, rho = -0.816057
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.521079, rho = -0.735407
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.801427, rho = -0.619397
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 58% (58/100) (classification)
Accuracy = 55% (550/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.361460, rho = -0.452521
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 88% (88/100) (classification)
Accuracy = 85.4% (854/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.100263, rho = -0.270356
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.798204
obj = -9.968791, rho = -0.194002
nSV = 82, nBSV = 77
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.674619
obj = -12.158061, rho = -0.200272
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.568486
obj = -14.782032, rho = -0.165384
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.480475
obj = -18.006917, rho = -0.104814
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.409968
obj = -21.781810, rho = -0.112201
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.345829
obj = -26.326115, rho = -0.114069
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.292277
obj = -31.660556, rho = -0.117272
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.245093
obj = -37.699969, rho = -0.183353
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.200532
obj = -45.234935, rho = -0.200017
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.163359
obj = -54.943150, rho = -0.247793
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.140000
obj = -67.847314, rho = -0.374450
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.125273
obj = -82.285365, rho = -0.715773
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.913302, rho = -0.918457
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.296902, rho = -0.882704
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.830689, rho = -0.831275
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.561265, rho = -0.757298
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.535091, rho = -0.650885
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.776417, rho = -0.497816
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 72% (72/100) (classification)
Accuracy = 67.7% (677/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.232029, rho = -0.277634
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 96% (96/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.772023, rho = -0.148904
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.755215
obj = -9.557941, rho = -0.121529
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.640905
obj = -11.672857, rho = -0.211563
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.565074
obj = -14.103203, rho = -0.153087
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.479823
obj = -16.636638, rho = -0.105838
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.384148
obj = -19.526883, rho = -0.099553
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.310627
obj = -23.155261, rho = -0.095167
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.251038
obj = -27.816805, rho = -0.108975
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.214976
obj = -33.858459, rho = -0.081004
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.181480
obj = -40.906674, rho = -0.076456
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.149472
obj = -49.463042, rho = -0.100826
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.132766
obj = -58.966171, rho = 0.002313
nSV = 15, nBSV = 11
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.108617
obj = -68.897590, rho = 0.139665
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.948768, rho = -0.913882
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.345058, rho = -0.876124
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.894042, rho = -0.821811
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.640153, rho = -0.743683
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.623234, rho = -0.631301
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 59% (59/100) (classification)
Accuracy = 54.7% (547/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.850789, rho = -0.469646
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 90% (90/100) (classification)
Accuracy = 87% (870/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.942906
obj = -6.267451, rho = -0.377921
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.866156
obj = -7.891963, rho = -0.322425
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -9.676645, rho = -0.269700
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.671569
obj = -11.618769, rho = -0.331866
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.563832
obj = -13.744253, rho = -0.319452
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.464137
obj = -16.137912, rho = -0.300841
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.380108
obj = -18.753330, rho = -0.357619
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.302981
obj = -21.737687, rho = -0.398957
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.246871
obj = -25.238109, rho = -0.375092
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.201419
obj = -29.254716, rho = -0.508306
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.156926
obj = -34.139404, rho = -0.541774
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.128890
obj = -40.004078, rho = -0.524217
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.105367
obj = -46.634781, rho = -0.716743
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.090328
obj = -53.341712, rho = -0.703824
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -0.818199, rho = 0.885347
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -1.163190, rho = 0.835624
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -1.644741, rho = 0.763554
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -2.307014, rho = 0.659628
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -3.196729, rho = 0.510358
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -4.346318, rho = 0.295675
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 72% (72/100) (classification)
Accuracy = 63.3% (633/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -5.730504, rho = -0.013532
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 88% (88/100) (classification)
Accuracy = 88.2% (882/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.787950
obj = -7.275078, rho = -0.090689
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 93% (93/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.699060
obj = -9.067545, rho = -0.127531
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 95% (95/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.607442
obj = -11.211991, rho = -0.100869
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.532112
obj = -13.662992, rho = -0.176551
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.444236
obj = -16.523774, rho = -0.207543
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.379977
obj = -19.951620, rho = -0.220026
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.318209
obj = -23.941948, rho = -0.242362
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.261380
obj = -28.770251, rho = -0.246881
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.212812
obj = -35.115345, rho = -0.214826
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.178191
obj = -43.754693, rho = -0.180817
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.156827
obj = -54.902559, rho = -0.018777
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.132686
obj = -69.561820, rho = 0.009479
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.117323
obj = -88.730558, rho = -0.047782
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.932359, rho = 0.869055
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.323718, rho = 0.811642
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.868032, rho = 0.729056
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.612433, rho = 0.610261
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.603422, rho = 0.439380
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.863799, rho = 0.193577
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 78% (78/100) (classification)
Accuracy = 78.5% (785/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.953598
obj = -6.335671, rho = -0.131221
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -7.968525, rho = -0.248747
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.766611
obj = -9.838444, rho = -0.222477
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.654191
obj = -12.114238, rho = -0.239990
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.571350
obj = -14.865366, rho = -0.232634
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.497591
obj = -17.932342, rho = -0.159073
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.418179
obj = -21.297997, rho = -0.121985
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.343391
obj = -25.200707, rho = -0.111041
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.278075
obj = -29.895092, rho = -0.120735
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.228416
obj = -35.794158, rho = -0.059098
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.191425
obj = -42.742720, rho = 0.040881
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.158144
obj = -50.934976, rho = 0.003324
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.131878
obj = -61.539637, rho = -0.095665
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.106230
obj = -74.808499, rho = -0.122372
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.912443, rho = 0.847316
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.295125, rho = 0.780372
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.827011, rho = 0.684077
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.553656, rho = 0.545560
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.519346, rho = 0.346311
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 55% (55/100) (classification)
Accuracy = 53% (530/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.743838, rho = 0.059701
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 82% (82/100) (classification)
Accuracy = 79.2% (792/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.919773
obj = -6.170183, rho = -0.233986
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 91% (91/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.834250
obj = -7.846449, rho = -0.236640
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.748722
obj = -9.843088, rho = -0.256193
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.655593
obj = -12.185448, rho = -0.197790
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.573199
obj = -15.029599, rho = -0.231813
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.486918
obj = -18.382177, rho = -0.250133
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.421286
obj = -22.226507, rho = -0.209290
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.355286
obj = -26.660848, rho = -0.221350
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.292240
obj = -31.957875, rho = -0.226982
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.244564
obj = -38.435894, rho = -0.228803
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.203648
obj = -46.528302, rho = -0.188974
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.171387
obj = -56.586052, rho = -0.189252
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.144008
obj = -68.491792, rho = -0.340480
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.120288
obj = -83.963514, rho = -0.386358
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.953819, rho = -0.905285
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.355509, rho = -0.863757
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.915667, rho = -0.804021
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.684898, rho = -0.718094
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.715817, rho = -0.594493
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.042357, rho = -0.416698
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 68% (68/100) (classification)
Accuracy = 71.9% (719/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.626933, rho = -0.160949
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.910183
obj = -8.402477, rho = -0.100663
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.811657
obj = -10.413627, rho = -0.102413
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.707544
obj = -12.766691, rho = -0.105446
nSV = 74, nBSV = 68
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.605182
obj = -15.447196, rho = -0.045203
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.510572
obj = -18.531189, rho = -0.016829
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.427273
obj = -22.090900, rho = -0.019390
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.358879
obj = -25.990941, rho = 0.046401
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.291887
obj = -30.433733, rho = 0.105970
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.240024
obj = -35.537514, rho = 0.047518
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.194128
obj = -41.543138, rho = 0.141477
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.165744
obj = -48.251492, rho = 0.135889
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.132393
obj = -53.659855, rho = 0.040021
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 438
nu = 0.105179
obj = -57.647025, rho = 0.086917
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.948742, rho = 0.812550
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.345004, rho = 0.730363
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.893929, rho = 0.612141
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.639920, rho = 0.442084
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.622753, rho = 0.197466
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 60% (60/100) (classification)
Accuracy = 63% (630/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.849794, rho = -0.154405
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 90% (90/100) (classification)
Accuracy = 89.7% (897/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.929261
obj = -6.282618, rho = -0.288189
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -7.996566, rho = -0.312130
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.766115
obj = -9.967437, rho = -0.276511
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.680090
obj = -12.238284, rho = -0.277485
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.577238
obj = -14.804447, rho = -0.245739
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.489734
obj = -17.813679, rho = -0.328342
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.411937
obj = -21.302888, rho = -0.407818
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.340050
obj = -25.489835, rho = -0.393150
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.285262
obj = -30.473598, rho = -0.376012
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.236995
obj = -36.278308, rho = -0.398732
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.196254
obj = -43.156053, rho = -0.363288
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.**.*
optimization finished, #iter = 213
nu = 0.156044
obj = -51.826880, rho = -0.348378
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.131184
obj = -63.819702, rho = -0.344592
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.111924
obj = -78.382865, rho = -0.328134
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.901005, rho = 0.918534
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.284072, rho = 0.882815
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.822285, rho = 0.831435
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -2.569977, rho = 0.757528
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.590660, rho = 0.651216
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.945400, rho = 0.498292
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -6.659361, rho = 0.278319
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 82% (82/100) (classification)
Accuracy = 82% (820/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.899591
obj = -8.652740, rho = 0.027908
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -10.955616, rho = -0.005135
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.722532
obj = -13.675354, rho = 0.006809
nSV = 76, nBSV = 70
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.639542
obj = -16.903010, rho = 0.003441
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.551708
obj = -20.663153, rho = 0.047658
nSV = 58, nBSV = 50
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.463516
obj = -25.265835, rho = 0.079157
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.394203
obj = -31.067712, rho = 0.102472
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.334804
obj = -38.031463, rho = 0.090626
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.287121
obj = -46.706997, rho = 0.116256
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.247156
obj = -57.298528, rho = 0.191423
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.213040
obj = -69.487879, rho = 0.207776
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.178790
obj = -84.203540, rho = 0.172070
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.146685
obj = -102.661940, rho = 0.156244
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.953678, rho = 0.872021
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.355216, rho = 0.815908
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.915060, rho = 0.735193
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.683643, rho = 0.619088
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.713222, rho = 0.452078
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.036986, rho = 0.211841
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 78% (78/100) (classification)
Accuracy = 81.1% (811/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.974813
obj = -6.616160, rho = -0.109934
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -8.456685, rho = -0.087629
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.815674
obj = -10.554909, rho = -0.146070
nSV = 83, nBSV = 78
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.714852
obj = -13.039942, rho = -0.104408
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.613802
obj = -15.874917, rho = -0.054494
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.516108
obj = -19.331885, rho = -0.047671
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.449739
obj = -23.393407, rho = 0.072946
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.372442
obj = -27.974218, rho = 0.115944
nSV = 41, nBSV = 32
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.310122
obj = -33.680598, rho = 0.041401
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.255239
obj = -40.899151, rho = 0.101524
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.218930
obj = -49.488845, rho = 0.261550
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.192135
obj = -59.018605, rho = 0.270662
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.156132
obj = -68.585394, rho = 0.349232
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.127383
obj = -78.961344, rho = 0.273599
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.972743, rho = -0.015131
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.382052, rho = -0.021765
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.952443, rho = -0.031307
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.734894, rho = -0.045034
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.781723, rho = -0.064779
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.124722, rho = -0.093181
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.986389
obj = -6.722283, rho = -0.116841
nSV = 100, nBSV = 98
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.930383
obj = -8.506907, rho = -0.109519
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -10.531909, rho = -0.044437
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.710162
obj = -12.892514, rho = -0.008855
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.602218
obj = -15.666768, rho = -0.027596
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.517895
obj = -18.973331, rho = -0.068351
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.435818
obj = -22.913219, rho = -0.038186
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.367701
obj = -27.355369, rho = -0.034193
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 174
nu = 0.300160
obj = -32.665762, rho = -0.093457
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.250894
obj = -39.188769, rho = -0.079467
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.207986
obj = -47.103482, rho = -0.162024
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.172183
obj = -57.346883, rho = -0.170174
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.150667
obj = -69.258172, rho = -0.058966
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.126530
obj = -82.084926, rho = -0.062745
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -0.859356, rho = -0.949258
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -1.223123, rho = -0.927621
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -1.732462, rho = -0.895886
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -2.436319, rho = -0.850237
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -3.389189, rho = -0.784574
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -4.636536, rho = -0.690120
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 61% (61/100) (classification)
Accuracy = 57.4% (574/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -6.175641, rho = -0.554254
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 82% (82/100) (classification)
Accuracy = 83.5% (835/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.852888
obj = -7.906422, rho = -0.377600
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.759714
obj = -9.863602, rho = -0.299034
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.661402
obj = -12.163150, rho = -0.279135
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.570116
obj = -14.815088, rho = -0.257000
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.493237
obj = -17.975655, rho = -0.252720
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.411208
obj = -21.587891, rho = -0.232727
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.341784
obj = -25.870038, rho = -0.246232
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.288238
obj = -30.959445, rho = -0.234144
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.243446
obj = -36.700841, rho = -0.211767
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.199265
obj = -43.028862, rho = -0.216907
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.165518
obj = -50.445272, rho = -0.252602
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 256
nu = 0.132720
obj = -58.467466, rho = -0.282623
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.110339
obj = -68.111682, rho = -0.275659
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -0.844096, rho = -0.942609
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -1.204160, rho = -0.917446
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -1.711370, rho = -0.881250
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -2.418776, rho = -0.829184
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -3.390433, rho = -0.754290
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -4.693115, rho = -0.646559
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -6.370392, rho = -0.491593
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 76% (76/100) (classification)
Accuracy = 69.5% (695/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.376351, rho = -0.268682
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -10.615018, rho = -0.252507
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.720000
obj = -13.196516, rho = -0.241212
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.625184
obj = -15.991945, rho = -0.275160
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.530615
obj = -19.205653, rho = -0.226038
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.438222
obj = -23.007009, rho = -0.228709
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.364081
obj = -27.688481, rho = -0.281888
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.310543
obj = -33.350533, rho = -0.187850
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.262139
obj = -39.813303, rho = -0.131775
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 331
nu = 0.222350
obj = -46.636844, rho = -0.168011
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*....*
optimization finished, #iter = 656
nu = 0.177611
obj = -53.864912, rho = -0.255892
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.139353
obj = -62.833449, rho = -0.221534
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.114451
obj = -74.638760, rho = -0.255470
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.861972, rho = 0.918516
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.228534, rho = 0.882789
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.743659, rho = 0.831398
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.459489, rho = 0.757474
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -3.437130, rho = 0.651139
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -4.735733, rho = 0.498181
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.380892, rho = 0.278159
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 77% (77/100) (classification)
Accuracy = 74.9% (749/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.874701
obj = -8.286870, rho = -0.019386
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.791633
obj = -10.408201, rho = -0.096199
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.691658
obj = -12.912993, rho = -0.085981
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.621070
obj = -15.765394, rho = 0.033032
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.533346
obj = -18.726138, rho = 0.007554
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.440000
obj = -21.908202, rho = 0.001036
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.358882
obj = -25.359005, rho = -0.099590
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.291822
obj = -29.203203, rho = -0.067675
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.237042
obj = -33.487071, rho = -0.111543
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.186868
obj = -37.872079, rho = -0.119349
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.150592
obj = -42.586982, rho = -0.202736
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.119461
obj = -46.785739, rho = -0.201106
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.093487
obj = -50.093912, rho = -0.163256
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.859777, rho = -0.935171
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.223992, rho = -0.906747
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.734262, rho = -0.865860
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -2.440051, rho = -0.806800
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -3.396911, rho = -0.722092
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -4.652514, rho = -0.600243
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 59% (59/100) (classification)
Accuracy = 53.1% (531/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -6.208702, rho = -0.424970
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 86% (86/100) (classification)
Accuracy = 81.8% (818/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.843142
obj = -7.959802, rho = -0.241185
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.760822
obj = -9.945370, rho = -0.175746
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.662261
obj = -12.331962, rho = -0.186485
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.578934
obj = -15.040824, rho = -0.140884
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.492187
obj = -18.263662, rho = -0.153816
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.419385
obj = -22.200018, rho = -0.186541
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.349132
obj = -26.902704, rho = -0.195710
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.295204
obj = -32.624194, rho = -0.132642
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.249067
obj = -39.420916, rho = -0.167504
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.209363
obj = -47.969742, rho = -0.081901
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.172424
obj = -58.939824, rho = -0.085818
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.150770
obj = -72.663775, rho = -0.127486
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.127382
obj = -89.191586, rho = -0.240360
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.935125, rho = 0.886931
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.329443, rho = 0.837356
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.879875, rho = 0.766045
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.636940, rho = 0.663468
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.654129, rho = 0.515915
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.968720, rho = 0.303668
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 68% (68/100) (classification)
Accuracy = 64.9% (649/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.552249, rho = -0.001639
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.882141
obj = -8.362113, rho = -0.056182
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.792308
obj = -10.589484, rho = -0.152672
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.716070
obj = -13.182631, rho = -0.209286
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.620000
obj = -16.093579, rho = -0.160487
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.512601
obj = -19.670668, rho = -0.133387
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.436143
obj = -24.299694, rho = -0.158266
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.382206
obj = -30.018622, rho = -0.007731
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.328551
obj = -36.591474, rho = 0.068908
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.285697
obj = -44.514897, rho = -0.053096
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.240982
obj = -52.789813, rho = -0.039565
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.201745
obj = -61.512976, rho = 0.026439
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.164046
obj = -71.337804, rho = -0.014547
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.131905
obj = -82.561475, rho = 0.002107
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -0.803547, rho = -0.944910
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.145486, rho = -0.920755
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.626254, rho = -0.886010
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -2.294865, rho = -0.835540
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -3.209129, rho = -0.763433
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -4.425980, rho = -0.660214
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.820000
obj = -5.973020, rho = -0.511117
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 75% (75/100) (classification)
Accuracy = 66.5% (665/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.820000
obj = -7.777616, rho = -0.296504
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 94% (94/100) (classification)
Accuracy = 92.7% (927/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.745406
obj = -9.775197, rho = -0.191642
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.663444
obj = -12.031938, rho = -0.119149
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.571643
obj = -14.628379, rho = -0.146550
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.470424
obj = -17.727718, rho = -0.163815
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.399107
obj = -21.672486, rho = -0.144005
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.328989
obj = -26.738349, rho = -0.146574
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.291747
obj = -33.091422, rho = 0.010568
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.249210
obj = -40.713383, rho = -0.107698
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.217087
obj = -49.816420, rho = -0.138558
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.177412
obj = -61.064121, rho = -0.172868
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.155079
obj = -76.160360, rho = -0.196684
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.138734
obj = -92.758189, rho = -0.073393
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -0.784265, rho = 0.936584
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -1.118201, rho = 0.908780
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -1.587942, rho = 0.868784
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -2.241687, rho = 0.811252
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.800000
obj = -3.136642, rho = 0.727709
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.800000
obj = -4.329998, rho = 0.608323
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.800000
obj = -5.852097, rho = 0.436593
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 73% (73/100) (classification)
Accuracy = 65.3% (653/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -7.639152, rho = 0.189567
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 91% (91/100) (classification)
Accuracy = 88.9% (889/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -9.563599, rho = 0.046061
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 96% (96/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.645332
obj = -11.749578, rho = -0.046212
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.552733
obj = -14.412661, rho = 0.016277
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.476184
obj = -17.319139, rho = 0.040101
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.401463
obj = -20.812237, rho = -0.017883
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.338956
obj = -24.454496, rho = -0.091043
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.274701
obj = -28.763274, rho = -0.037415
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.229575
obj = -33.633642, rho = -0.077799
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.188767
obj = -38.822043, rho = -0.147925
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.151720
obj = -44.053578, rho = -0.293724
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.117298
obj = -50.104925, rho = -0.304220
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.093785
obj = -57.747834, rho = -0.351528
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -0.822367, rho = 0.923268
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -1.171813, rho = 0.889624
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -1.662584, rho = 0.841230
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -2.343931, rho = 0.771617
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -3.273111, rho = 0.671483
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -4.504362, rho = 0.527444
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 58% (58/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -6.057519, rho = 0.320252
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 79% (79/100) (classification)
Accuracy = 74.9% (749/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.834629
obj = -7.841313, rho = 0.041230
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 93% (93/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.744602
obj = -9.823340, rho = 0.022499
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.660000
obj = -12.189370, rho = 0.000963
nSV = 67, nBSV = 65
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.566579
obj = -14.976772, rho = 0.026835
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.499067
obj = -18.186060, rho = -0.047243
nSV = 54, nBSV = 45
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.418175
obj = -21.856309, rho = -0.128327
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.351034
obj = -26.206387, rho = -0.167586
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.286843
obj = -31.663269, rho = -0.192594
nSV = 31, nBSV = 28
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.249256
obj = -37.784994, rho = -0.305838
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.202876
obj = -44.648876, rho = -0.301638
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.162855
obj = -53.598804, rho = -0.265946
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.137340
obj = -65.173337, rho = -0.296787
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 262
nu = 0.115787
obj = -79.829947, rho = -0.358729
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -0.802720, rho = 0.942321
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.143775, rho = 0.917031
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.622713, rho = 0.880653
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -2.287534, rho = 0.827991
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -3.193960, rho = 0.752574
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -4.394592, rho = 0.644091
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -5.908071, rho = 0.488042
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 78% (78/100) (classification)
Accuracy = 63.4% (634/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.643227, rho = 0.263574
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 93% (93/100) (classification)
Accuracy = 90.8% (908/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.742633
obj = -9.486309, rho = 0.142759
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.640365
obj = -11.597296, rho = 0.057091
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.542717
obj = -14.098036, rho = 0.071579
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.460202
obj = -17.125048, rho = 0.020983
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.382178
obj = -20.926874, rho = -0.004483
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.333305
obj = -25.358856, rho = 0.017705
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.281641
obj = -30.540563, rho = 0.014672
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.240750
obj = -36.032910, rho = -0.083315
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.194527
obj = -42.008148, rho = -0.087674
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.157983
obj = -49.395138, rho = -0.114728
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.133220
obj = -58.153629, rho = -0.111252
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.110459
obj = -66.740493, rho = -0.222796
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.951374, rho = 0.864805
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.350449, rho = 0.805529
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.905197, rho = 0.720263
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.663234, rho = 0.597613
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.670992, rho = 0.421186
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 55% (55/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.949607, rho = 0.167405
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 87% (87/100) (classification)
Accuracy = 90.1% (901/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.953635
obj = -6.453410, rho = -0.006661
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.889083
obj = -8.224250, rho = -0.025411
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.810798
obj = -10.168872, rho = 0.028718
nSV = 82, nBSV = 77
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.680000
obj = -12.390934, rho = 0.028095
nSV = 69, nBSV = 67
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.589111
obj = -15.004146, rho = 0.058490
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.504096
obj = -18.070633, rho = 0.000187
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.420345
obj = -21.375243, rho = -0.020110
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.342279
obj = -25.377293, rho = -0.061145
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.286693
obj = -30.215608, rho = -0.161565
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.239309
obj = -35.533631, rho = -0.101145
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*..*
optimization finished, #iter = 324
nu = 0.192505
obj = -41.472276, rho = -0.168686
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.155121
obj = -49.050300, rho = -0.237071
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*..*
optimization finished, #iter = 370
nu = 0.127211
obj = -58.613605, rho = -0.270613
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.106967
obj = -70.130144, rho = -0.268500
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.861922, rho = -0.948436
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.228429, rho = -0.925828
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.743442, rho = -0.893307
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -2.459039, rho = -0.846527
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.436200, rho = -0.779237
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.733809, rho = -0.682444
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 59% (59/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.376911, rho = -0.543212
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 77% (77/100) (classification)
Accuracy = 75.3% (753/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.851901
obj = -8.298153, rho = -0.388201
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 92% (92/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.801997
obj = -10.521278, rho = -0.242793
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.704134
obj = -12.986491, rho = -0.258062
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 95% (95/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.604597
obj = -15.966590, rho = -0.218461
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.515624
obj = -19.628584, rho = -0.238891
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.452612
obj = -24.050117, rho = -0.294083
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.388858
obj = -28.938667, rho = -0.310958
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.325520
obj = -34.580428, rho = -0.280019
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.262165
obj = -41.205308, rho = -0.280803
nSV = 32, nBSV = 22
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.220349
obj = -49.637128, rho = -0.243077
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.188223
obj = -59.865390, rho = -0.126449
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*.*
optimization finished, #iter = 323
nu = 0.154661
obj = -71.217510, rho = -0.081548
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.127421
obj = -85.999716, rho = -0.153705
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -0.860265, rho = 0.906970
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.880000
obj = -1.225003, rho = 0.866440
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.880000
obj = -1.736353, rho = 0.807881
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.880000
obj = -2.444371, rho = 0.723646
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.880000
obj = -3.405850, rho = 0.602479
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -4.671011, rho = 0.428186
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 60% (60/100) (classification)
Accuracy = 53% (530/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -6.246975, rho = 0.177239
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 87% (87/100) (classification)
Accuracy = 78.5% (785/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.832345
obj = -8.064127, rho = 0.000168
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 93% (93/100) (classification)
Accuracy = 90.7% (907/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.766644
obj = -10.225904, rho = -0.130499
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.686528
obj = -12.692719, rho = -0.105670
nSV = 70, nBSV = 68
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.598490
obj = -15.521787, rho = -0.066494
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.520665
obj = -18.709737, rho = -0.031542
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.443017
obj = -22.114269, rho = -0.026820
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.363333
obj = -25.969493, rho = -0.062808
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.291087
obj = -30.208277, rho = -0.104684
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.234067
obj = -35.639931, rho = -0.112994
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.197386
obj = -42.256690, rho = -0.007416
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.162262
obj = -49.386936, rho = 0.008214
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 288
nu = 0.129124
obj = -57.576164, rho = 0.054378
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*..*
optimization finished, #iter = 501
nu = 0.104520
obj = -67.496972, rho = 0.151450
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.889932, rho = -0.919935
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.261158, rho = -0.884831
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.774874, rho = -0.834334
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.471877, rho = -0.761698
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.387678, rho = -0.657215
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 61% (61/100) (classification)
Accuracy = 55.6% (556/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.525402, rho = -0.506921
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 82% (82/100) (classification)
Accuracy = 79.2% (792/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -5.849520, rho = -0.388115
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 93% (93/100) (classification)
Accuracy = 91.3% (913/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.783595
obj = -7.432647, rho = -0.325843
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.720000
obj = -9.292873, rho = -0.245436
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.633950
obj = -11.316051, rho = -0.168051
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.543763
obj = -13.584654, rho = -0.173627
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.455187
obj = -16.194835, rho = -0.216606
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.378744
obj = -19.116049, rho = -0.199938
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.317083
obj = -22.472836, rho = -0.165332
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.259455
obj = -25.850152, rho = -0.186720
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.207860
obj = -29.505582, rho = -0.118498
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.164938
obj = -33.530002, rho = -0.127047
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.133368
obj = -37.504122, rho = -0.109594
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*........*
optimization finished, #iter = 890
nu = 0.104257
obj = -41.188951, rho = -0.163859
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.081363
obj = -44.544551, rho = -0.213012
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.969054, rho = 0.000152
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.374419, rho = 0.000219
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.936650, rho = 0.000315
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.702215, rho = 0.000453
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.714106, rho = 0.000652
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.984812, rho = 0.000938
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.993906
obj = -6.430629, rho = 0.004828
nSV = 100, nBSV = 97
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.902065
obj = -7.970460, rho = -0.066051
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -9.634363, rho = -0.053340
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.666730
obj = -11.392419, rho = -0.065518
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.551225
obj = -13.395867, rho = -0.049163
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.451245
obj = -15.741421, rho = -0.115098
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.363572
obj = -18.403788, rho = -0.122733
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.292297
obj = -21.742724, rho = -0.090763
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.242651
obj = -25.895806, rho = -0.109813
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.204292
obj = -30.536297, rho = -0.105389
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.175460
obj = -35.012171, rho = -0.005465
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.134400
obj = -39.618372, rho = 0.037554
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.108002
obj = -44.816252, rho = 0.055540
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.082375
obj = -50.889757, rho = 0.021173
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -0.920951, rho = 0.907446
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.312729, rho = 0.866865
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.863436, rho = 0.808492
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.629025, rho = 0.724526
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -3.675295, rho = 0.603744
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -5.066519, rho = 0.430006
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 54% (54/100) (classification)
Accuracy = 53.7% (537/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -6.832289, rho = 0.180092
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 76% (76/100) (classification)
Accuracy = 83.6% (836/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.930218
obj = -8.887336, rho = -0.149813
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.842939
obj = -11.214175, rho = -0.239143
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.754670
obj = -13.956165, rho = -0.181606
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.660217
obj = -17.027207, rho = -0.258235
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.558484
obj = -20.447279, rho = -0.232518
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.472067
obj = -24.529130, rho = -0.280225
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.389410
obj = -29.363315, rho = -0.292453
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.326951
obj = -35.159392, rho = -0.267000
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.268248
obj = -42.253896, rho = -0.277831
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.229899
obj = -50.829732, rho = -0.175564
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 276
nu = 0.186184
obj = -60.667610, rho = -0.150000
nSV = 24, nBSV = 13
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.151384
obj = -73.977796, rho = -0.135512
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.130796
obj = -91.907526, rho = -0.001387
nSV = 15, nBSV = 11
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.933603, rho = -0.905465
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.326293, rho = -0.864016
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.873358, rho = -0.804394
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.623454, rho = -0.718630
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.626225, rho = -0.595264
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.910983, rho = -0.417807
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 68% (68/100) (classification)
Accuracy = 67.5% (675/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.432782, rho = -0.162545
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.119359, rho = -0.084565
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.784018
obj = -10.102598, rho = -0.091571
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.685864
obj = -12.343398, rho = -0.053352
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.590673
obj = -14.922120, rho = -0.097803
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.490067
obj = -17.849772, rho = -0.143038
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.400335
obj = -21.583409, rho = -0.132929
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.338667
obj = -26.432150, rho = -0.132019
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.290151
obj = -32.154320, rho = -0.165846
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.247521
obj = -38.898311, rho = -0.187788
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.204855
obj = -46.901240, rho = -0.207864
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.167732
obj = -57.529689, rho = -0.232186
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.148300
obj = -70.900024, rho = -0.367330
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.127973
obj = -85.821633, rho = -0.316667
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.877936, rho = -0.936401
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.8% (528/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.248950, rho = -0.908515
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.8% (528/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.767759, rho = -0.868404
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.8% (528/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -2.483255, rho = -0.810706
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.8% (528/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.448762, rho = -0.727710
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 53.1% (531/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.705797, rho = -0.608324
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 77% (77/100) (classification)
Accuracy = 71% (710/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.241271, rho = -0.436593
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 90% (90/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.830412
obj = -8.027465, rho = -0.384348
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.757930
obj = -10.220271, rho = -0.339122
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.678272
obj = -12.783670, rho = -0.307145
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.600000
obj = -15.825719, rho = -0.280089
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.508335
obj = -19.379029, rho = -0.230560
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.437972
obj = -23.666640, rho = -0.210298
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.370762
obj = -28.962136, rho = -0.139546
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.320832
obj = -35.180106, rho = -0.160909
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.272894
obj = -42.152177, rho = -0.227664
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.227601
obj = -50.177184, rho = -0.285427
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.191809
obj = -59.706810, rho = -0.191673
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.158239
obj = -70.393289, rho = -0.163500
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.133401
obj = -82.064669, rho = -0.242640
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -0.920016, rho = 0.902812
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.940000
obj = -1.310797, rho = 0.858949
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.940000
obj = -1.859440, rho = 0.797105
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -2.620756, rho = 0.708146
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -3.658185, rho = 0.580182
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -5.031117, rho = 0.396146
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 57% (57/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -6.759039, rho = 0.131387
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 81% (81/100) (classification)
Accuracy = 82% (820/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.919500
obj = -8.752735, rho = -0.096355
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.843681
obj = -11.045090, rho = -0.117440
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.747643
obj = -13.615692, rho = -0.144828
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.646056
obj = -16.479762, rho = -0.171331
nSV = 68, nBSV = 61
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.544010
obj = -19.837927, rho = -0.166615
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.456278
obj = -23.690906, rho = -0.187802
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.380044
obj = -28.204089, rho = -0.202694
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.316981
obj = -33.425888, rho = -0.248151
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.266681
obj = -39.312772, rho = -0.391890
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 261
nu = 0.214733
obj = -46.039658, rho = -0.386498
nSV = 26, nBSV = 15
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 312
nu = 0.169525
obj = -54.566052, rho = -0.391653
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.138885
obj = -65.885240, rho = -0.415070
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.114100
obj = -80.808793, rho = -0.382323
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.934161, rho = 0.872176
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.1% (461/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.327448, rho = 0.816131
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.1% (461/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.875749, rho = 0.735514
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.1% (461/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.628402, rho = 0.619550
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 46.1% (461/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.636462, rho = 0.452742
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47% (470/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.932165, rho = 0.212797
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 79% (79/100) (classification)
Accuracy = 73.6% (736/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.476611, rho = -0.132352
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.881451
obj = -8.248906, rho = -0.217165
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.793832
obj = -10.374541, rho = -0.129289
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.697135
obj = -12.798489, rho = -0.044472
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.599346
obj = -15.671604, rho = -0.002876
nSV = 63, nBSV = 56
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.506914
obj = -19.186611, rho = 0.049625
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.437651
obj = -23.354396, rho = 0.006161
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.364125
obj = -28.442576, rho = 0.010082
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.304562
obj = -34.855085, rho = 0.037866
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.266835
obj = -42.778178, rho = 0.203319
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.230291
obj = -51.253497, rho = 0.410937
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.195460
obj = -61.456777, rho = 0.473988
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*...*
optimization finished, #iter = 310
nu = 0.158070
obj = -72.695576, rho = 0.504926
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.131509
obj = -87.376895, rho = 0.476446
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.915145, rho = -0.931369
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.300715, rho = -0.901278
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.838577, rho = -0.857993
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.577589, rho = -0.795730
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.568866, rho = -0.706168
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.846302, rho = -0.577338
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 77% (77/100) (classification)
Accuracy = 67.5% (675/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.376630, rho = -0.392021
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 91% (910/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.862534
obj = -8.118026, rho = -0.317065
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -10.209113, rho = -0.285168
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.695419
obj = -12.513693, rho = -0.197549
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.590434
obj = -15.162897, rho = -0.160149
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.503797
obj = -18.300343, rho = -0.129052
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.423359
obj = -21.852285, rho = -0.095360
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.353921
obj = -25.959141, rho = -0.154360
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.296495
obj = -30.638904, rho = -0.199931
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*..*
optimization finished, #iter = 211
nu = 0.235938
obj = -35.966108, rho = -0.179403
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.192425
obj = -42.898901, rho = -0.175495
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.167770
obj = -50.290517, rho = -0.360235
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 117
nu = 0.131721
obj = -58.165794, rho = -0.417692
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.104904
obj = -68.603425, rho = -0.444271
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.969516, rho = -0.029378
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.375375, rho = -0.042258
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.938627, rho = -0.060786
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.706307, rho = -0.087438
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.722573, rho = -0.125776
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.002332, rho = -0.180922
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.507846, rho = -0.162873
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.894554
obj = -8.334664, rho = -0.155540
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.798936
obj = -10.459949, rho = -0.196093
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.701156
obj = -12.929994, rho = -0.181205
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.607290
obj = -15.832262, rho = -0.177816
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.504895
obj = -19.453111, rho = -0.155035
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.431811
obj = -24.093694, rho = -0.169775
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.372667
obj = -29.883715, rho = -0.171939
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.328599
obj = -36.998365, rho = -0.145283
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.276327
obj = -45.536592, rho = -0.062682
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.233890
obj = -56.798432, rho = -0.107420
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.206233
obj = -70.636630, rho = -0.098296
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.174508
obj = -88.282195, rho = -0.126624
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.155766
obj = -110.425918, rho = -0.175543
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.878965, rho = 0.892298
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.251083, rho = 0.844437
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.772171, rho = 0.776231
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.492383, rho = 0.678119
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.467650, rho = 0.536991
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.744880, rho = 0.333985
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 61% (61/100) (classification)
Accuracy = 62% (620/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -6.322137, rho = 0.041970
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 89% (89/100) (classification)
Accuracy = 86.1% (861/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.106176, rho = -0.167954
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -10.159538, rho = -0.225622
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.687257
obj = -12.533301, rho = -0.195782
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.587712
obj = -15.232983, rho = -0.107462
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.504642
obj = -18.371217, rho = -0.137111
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.421828
obj = -22.044505, rho = -0.092609
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.343625
obj = -26.594372, rho = -0.086792
nSV = 42, nBSV = 32
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.295902
obj = -32.273944, rho = -0.106787
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.246740
obj = -38.697280, rho = -0.090352
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.208038
obj = -46.408941, rho = -0.162529
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 263
nu = 0.172599
obj = -55.641088, rho = -0.189282
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.144476
obj = -66.653323, rho = -0.200479
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.123110
obj = -79.190836, rho = -0.214668
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.859507, rho = -0.940220
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.223433, rho = -0.914010
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.733104, rho = -0.876307
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.437648, rho = -0.822074
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -3.391938, rho = -0.744062
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -4.642224, rho = -0.631846
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.187410, rho = -0.470429
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 81% (81/100) (classification)
Accuracy = 83.6% (836/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.897005, rho = -0.266416
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.778619
obj = -9.717567, rho = -0.158307
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.656196
obj = -11.782530, rho = -0.133614
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.558950
obj = -14.281678, rho = -0.103149
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.478058
obj = -17.120760, rho = -0.114793
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.397663
obj = -20.236499, rho = -0.105332
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.333553
obj = -23.649217, rho = -0.152730
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.265022
obj = -27.306540, rho = -0.095365
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.217414
obj = -31.587034, rho = 0.012644
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.172853
obj = -36.509019, rho = 0.060451
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.142012
obj = -41.878182, rho = 0.000608
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.114142
obj = -47.363095, rho = -0.013372
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.091269
obj = -53.076195, rho = -0.115852
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -0.857777, rho = 0.894137
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 45.4% (454/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -1.219856, rho = 0.847602
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 45.4% (454/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.725703, rho = 0.780783
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 45.4% (454/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -2.422336, rho = 0.685028
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 45.4% (454/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -3.360257, rho = 0.546030
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 45.4% (454/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -4.576673, rho = 0.346987
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 62% (62/100) (classification)
Accuracy = 58.4% (584/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.051777, rho = 0.060278
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 92% (92/100) (classification)
Accuracy = 87.6% (876/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.846759
obj = -7.640123, rho = -0.152844
nSV = 87, nBSV = 82
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.747941
obj = -9.372696, rho = -0.099891
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.635163
obj = -11.321233, rho = -0.096789
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.526968
obj = -13.721624, rho = -0.116214
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.447475
obj = -16.673041, rho = -0.154369
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.380299
obj = -20.154771, rho = -0.090708
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.320932
obj = -24.358185, rho = -0.053842
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.270075
obj = -29.259037, rho = 0.088576
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.224924
obj = -35.130326, rho = 0.088783
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.184999
obj = -42.431976, rho = 0.002511
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.150249
obj = -52.182107, rho = -0.028572
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.137098
obj = -64.138693, rho = -0.119891
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.115852
obj = -76.438644, rho = -0.198518
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.892661, rho = -0.933869
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.266806, rho = -0.904874
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.786559, rho = -0.863166
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.496059, rho = -0.803004
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.437712, rho = -0.716632
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.628931, rho = -0.592389
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 79% (79/100) (classification)
Accuracy = 68.4% (684/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.898944
obj = -6.011156, rho = -0.453505
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 91% (91/100) (classification)
Accuracy = 87.5% (875/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -7.644670, rho = -0.368009
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.723631
obj = -9.530041, rho = -0.342439
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.633745
obj = -11.829508, rho = -0.355524
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 96% (96/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.550878
obj = -14.668046, rho = -0.331727
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.481593
obj = -18.008700, rho = -0.330606
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.410777
obj = -21.906995, rho = -0.259840
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.344720
obj = -26.583339, rho = -0.238346
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.296352
obj = -32.183976, rho = -0.110367
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.243478
obj = -39.026946, rho = -0.180945
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.202563
obj = -47.663429, rho = -0.181289
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.171214
obj = -59.167879, rho = -0.234015
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.148506
obj = -73.641862, rho = -0.150224
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.129468
obj = -91.182677, rho = -0.263227
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.954560, rho = -0.919160
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.357043, rho = -0.883716
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.918839, rho = -0.832732
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.691462, rho = -0.759393
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.729401, rho = -0.653899
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.070463, rho = -0.502151
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 78% (78/100) (classification)
Accuracy = 76.9% (769/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.685087, rho = -0.292433
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 90% (90/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -8.555245, rho = -0.324625
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -10.867532, rho = -0.309138
nSV = 80, nBSV = 79
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.718516
obj = -13.687599, rho = -0.187845
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.640000
obj = -16.993559, rho = -0.211581
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.554896
obj = -20.739622, rho = -0.238795
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.470090
obj = -25.149513, rho = -0.220855
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.399450
obj = -30.407432, rho = -0.197277
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.348251
obj = -36.360932, rho = -0.320159
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.283588
obj = -42.694665, rho = -0.359656
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.230637
obj = -50.059641, rho = -0.395496
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.188521
obj = -59.084204, rho = -0.345578
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.151663
obj = -70.333305, rho = -0.364054
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.122053
obj = -85.661753, rho = -0.364068
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.894073, rho = 0.874876
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.269728, rho = 0.820016
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.792606, rho = 0.741102
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.508566, rho = 0.627587
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.463592, rho = 0.464303
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.682478, rho = 0.229427
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 77% (77/100) (classification)
Accuracy = 70% (700/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.115339, rho = -0.108431
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 95% (95/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.849071
obj = -7.693300, rho = -0.128081
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.758036
obj = -9.454752, rho = -0.192852
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.642008
obj = -11.397182, rho = -0.150467
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.553494
obj = -13.611117, rho = -0.147275
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.454335
obj = -16.064710, rho = -0.177128
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.373786
obj = -19.056229, rho = -0.191733
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.307483
obj = -22.469747, rho = -0.182214
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.250036
obj = -26.747245, rho = -0.250654
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.203392
obj = -32.000177, rho = -0.224318
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.174124
obj = -38.580119, rho = -0.292820
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.147962
obj = -45.721756, rho = -0.294870
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.119961
obj = -53.624244, rho = -0.330269
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.098977
obj = -63.080951, rho = -0.296656
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949899, rho = 0.873142
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.347397, rho = 0.817522
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.898881, rho = 0.737514
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.650165, rho = 0.622427
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.643952, rho = 0.456880
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.893657, rho = 0.218749
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 82% (82/100) (classification)
Accuracy = 79.5% (795/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.319251, rho = -0.118490
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.881140
obj = -7.855546, rho = -0.029586
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.759419
obj = -9.625547, rho = -0.004648
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.655587
obj = -11.717044, rho = -0.070347
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.554116
obj = -14.158150, rho = -0.083631
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.466320
obj = -17.022096, rho = -0.119075
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.389260
obj = -20.400627, rho = -0.092814
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.328721
obj = -24.455872, rho = -0.183346
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.276339
obj = -29.095402, rho = -0.162373
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.231189
obj = -34.045577, rho = -0.365384
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.190055
obj = -39.249661, rho = -0.505761
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 167
nu = 0.155686
obj = -43.857060, rho = -0.519973
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.122084
obj = -48.427375, rho = -0.468681
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 309
nu = 0.093922
obj = -51.797410, rho = -0.480843
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.916975, rho = 0.906848
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.304500, rho = 0.866005
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.846410, rho = 0.807255
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.593796, rho = 0.722746
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -3.602401, rho = 0.601184
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.915691, rho = 0.426324
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 60% (60/100) (classification)
Accuracy = 55.1% (551/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.520206, rho = 0.174795
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 93% (930/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900576
obj = -8.297078, rho = 0.022702
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.811112
obj = -10.153295, rho = 0.043431
nSV = 85, nBSV = 78
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.686629
obj = -12.299604, rho = 0.074983
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.584754
obj = -14.827352, rho = 0.028094
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.492976
obj = -17.683135, rho = -0.047764
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.399180
obj = -21.173980, rho = -0.041019
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.337166
obj = -25.651110, rho = -0.084631
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.283402
obj = -31.033094, rho = -0.158610
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.240777
obj = -37.446838, rho = -0.161121
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.195208
obj = -45.164236, rho = -0.158465
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.163075
obj = -55.142438, rho = -0.168468
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.136478
obj = -68.484676, rho = -0.238840
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.118130
obj = -86.024262, rho = -0.201497
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.876921, rho = -0.934283
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.246851, rho = -0.905470
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.763416, rho = -0.864023
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.474267, rho = -0.804404
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.430166, rho = -0.718645
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.667320, rho = -0.595285
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 66% (66/100) (classification)
Accuracy = 64.7% (647/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.892874
obj = -6.162541, rho = -0.431028
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 89% (89/100) (classification)
Accuracy = 86.9% (869/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.848127
obj = -7.847461, rho = -0.250961
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.760627
obj = -9.747399, rho = -0.148075
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.670885
obj = -11.846024, rho = -0.015565
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.567893
obj = -14.156715, rho = -0.013840
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.469304
obj = -16.930070, rho = -0.017298
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.387308
obj = -20.207020, rho = -0.013808
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.317820
obj = -24.401500, rho = -0.032113
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.275600
obj = -29.258950, rho = -0.033132
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.226871
obj = -34.750246, rho = -0.043971
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.187625
obj = -41.402524, rho = -0.109194
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.154262
obj = -49.519714, rho = 0.070384
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.130830
obj = -59.098780, rho = 0.064317
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.109675
obj = -69.249233, rho = 0.077939
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.924546, rho = 0.811368
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.307553, rho = 0.728663
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.834583, rho = 0.609695
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.543224, rho = 0.438566
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.460218, rho = 0.192405
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 71% (71/100) (classification)
Accuracy = 65% (650/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.567490, rho = -0.161685
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 90% (90/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -5.837503, rho = -0.267747
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.795442
obj = -7.398290, rho = -0.238223
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.716812
obj = -9.221194, rho = -0.279716
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.627198
obj = -11.266385, rho = -0.256670
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.534768
obj = -13.611006, rho = -0.226652
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.453366
obj = -16.240522, rho = -0.183459
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.370649
obj = -19.302004, rho = -0.225149
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.313227
obj = -23.063362, rho = -0.149998
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.254816
obj = -27.483132, rho = -0.170636
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.217675
obj = -32.488403, rho = -0.221724
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.182656
obj = -37.555817, rho = -0.222172
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.150037
obj = -42.663180, rho = -0.198470
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 267
nu = 0.119933
obj = -47.313853, rho = -0.222564
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.095026
obj = -50.569618, rho = -0.226895
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -0.746067, rho = -0.960213
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -1.064392, rho = -0.942768
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -1.512892, rho = -0.917675
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -2.138597, rho = -0.881580
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -2.998420, rho = -0.829659
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -4.152003, rho = -0.754973
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -5.639166, rho = -0.647541
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 67% (67/100) (classification)
Accuracy = 55.2% (552/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -7.422049, rho = -0.493005
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 91% (91/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.719636
obj = -9.324872, rho = -0.339514
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.632063
obj = -11.489132, rho = -0.267388
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.537263
obj = -14.057131, rho = -0.195189
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.461900
obj = -17.095530, rho = -0.258972
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.400076
obj = -20.452706, rho = -0.440064
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.333469
obj = -24.120091, rho = -0.469749
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.274080
obj = -28.161486, rho = -0.513109
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 200
nu = 0.223275
obj = -32.689881, rho = -0.549908
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 170
nu = 0.176697
obj = -37.938808, rho = -0.544589
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.145490
obj = -44.548370, rho = -0.540402
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.123101
obj = -50.516487, rho = -0.493774
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.096944
obj = -56.473418, rho = -0.513572
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -0.840182, rho = 0.911188
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.196061, rho = 0.872248
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.694611, rho = 0.816235
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -2.384101, rho = 0.735663
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -3.318691, rho = 0.620000
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -4.544671, rho = 0.453389
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 58% (58/100) (classification)
Accuracy = 53.1% (531/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -6.063241, rho = 0.213728
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 81% (81/100) (classification)
Accuracy = 79.9% (799/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.846355
obj = -7.745054, rho = -0.073870
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.753212
obj = -9.555329, rho = -0.034258
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.649647
obj = -11.595831, rho = -0.055199
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.555632
obj = -13.959863, rho = -0.053841
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.462612
obj = -16.751580, rho = -0.096646
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.383021
obj = -20.131554, rho = -0.153375
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.322064
obj = -24.125349, rho = -0.139358
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.268624
obj = -28.895325, rho = -0.123549
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.230288
obj = -34.234832, rho = -0.132395
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.189039
obj = -39.940737, rho = -0.059139
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 222
nu = 0.149620
obj = -46.688830, rho = -0.024630
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.122738
obj = -55.350834, rho = -0.127040
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.101844
obj = -65.257992, rho = -0.225265
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -0.880321, rho = 0.927847
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -1.253886, rho = 0.896211
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.900000
obj = -1.777971, rho = 0.850695
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.504385, rho = 0.785217
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.492484, rho = 0.691045
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.796265, rho = 0.555584
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 56% (56/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.428461, rho = 0.360730
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 76% (76/100) (classification)
Accuracy = 74.2% (742/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.283101, rho = 0.122131
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.809545
obj = -10.203961, rho = -0.041477
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.695528
obj = -12.430692, rho = 0.014160
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.589894
obj = -14.988993, rho = 0.014217
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.496143
obj = -17.979484, rho = 0.018501
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.424591
obj = -21.371396, rho = -0.088446
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.345661
obj = -25.165308, rho = -0.120024
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.291606
obj = -29.294833, rho = -0.139951
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.236762
obj = -33.445850, rho = -0.116292
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.184796
obj = -38.137498, rho = -0.067144
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.145563
obj = -43.806560, rho = -0.066623
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.116988
obj = -50.368335, rho = -0.014313
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.097200
obj = -57.134888, rho = -0.086703
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
No description has been provided for this image
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt


def data(N,sigma):   
    w = np.ones(10)/np.sqrt(10)   
    w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)   
    w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)   
    x = np.zeros((4,10))   
    x[1,:] = x[0,:] + sigma*w1   
    x[2,:] = x[0,:] + sigma*w2   
    x[3,:] = x[2,:] + sigma*w1   
    X1 = x + sigma*matlib.repmat(w,4,1)/2   
    X2 = x - sigma*matlib.repmat(w,4,1)/2   
    X1 = matlib.repmat(X1,2*N,1)   
    X2 = matlib.repmat(X2,2*N,1)   
    X = np.concatenate((X1, X2), axis=0)   
    Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)   
    Z = np.random.permutation(16*N)   
    Z = Z[:N]   
    X = X[Z,:]   
    X = X + 0.2*sigma*np.random.randn(N,10)   
    Y = Y[Z]

    return X, Y

# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-2, 1, 20)  # Logarithmically spaced values between 0.01 and 10

# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 0.5

# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):

    # Generate data
    X_train, y_train = data(100,sigma)
    X_test, y_test = data(1000, sigma)

    for lam in lambda_values:
        
        # Train SVM
        svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
        param = svm_parameter(f'-t 0 -c {lam}')
        model = svm_train(svm_problem_setup, param)
        
        # Predict on training and test data
        i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
        i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
        
        # Calculate errors
        training_errors.append(100 - train_accuracy[0])  # Convert to error percentage
        test_errors.append(100 - test_accuracy[0])  # Convert to error percentage

# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)

avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)

lambda_values_log = np.log10(lambda_values)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')

plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.01,10) @ σ = 1')
plt.legend()
plt.show()
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.932719, rho = 0.857246
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.324464, rho = 0.794656
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.869574, rho = 0.704623
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.615625, rho = 0.575114
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.610025, rho = 0.388823
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.877462, rho = 0.120853
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 75% (75/100) (classification)
Accuracy = 77.5% (775/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.951705
obj = -6.364395, rho = -0.213655
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.857639
obj = -8.088236, rho = -0.202594
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.774771
obj = -10.175957, rho = -0.204820
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.690525
obj = -12.518759, rho = -0.110609
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.582858
obj = -15.208185, rho = -0.120108
nSV = 63, nBSV = 55
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.499728
obj = -18.583098, rho = -0.212492
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.421808
obj = -22.535245, rho = -0.217710
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.359568
obj = -27.362129, rho = -0.117363
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.302488
obj = -32.647133, rho = -0.132588
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.248027
obj = -39.256698, rho = -0.138070
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.209878
obj = -47.638099, rho = -0.266255
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.177147
obj = -57.635583, rho = -0.220701
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.145467
obj = -69.792219, rho = -0.186210
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.120792
obj = -85.877231, rho = -0.205660
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.898093, rho = 0.904199
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.278046, rho = 0.862195
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.809817, rho = 0.801774
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.544179, rho = 0.714861
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.537281, rho = 0.590619
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.834953, rho = 0.411125
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 61% (61/100) (classification)
Accuracy = 63.4% (634/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.430829, rho = 0.152933
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 88% (88/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.879999
obj = -8.241725, rho = -0.014584
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 94% (94/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.790686
obj = -10.332252, rho = -0.027441
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.684615
obj = -12.789375, rho = -0.037106
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.597582
obj = -15.716381, rho = -0.065441
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.515083
obj = -19.172420, rho = -0.108715
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.434847
obj = -23.386882, rho = -0.128425
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.364242
obj = -28.551892, rho = -0.152445
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.312247
obj = -34.822180, rho = -0.168637
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.268101
obj = -42.386101, rho = -0.187802
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.224555
obj = -51.119587, rho = -0.297038
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.187032
obj = -62.492587, rho = -0.214722
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.171410
obj = -74.631787, rho = -0.037012
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.136896
obj = -86.312663, rho = 0.003816
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.968730, rho = -0.021037
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.373748, rho = -0.030261
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.935262, rho = -0.043529
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.699343, rho = -0.062614
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.708163, rho = -0.090067
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.972516, rho = -0.129557
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.417230, rho = -0.177928
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 93% (93/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.889353
obj = -8.028078, rho = -0.206359
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.770971
obj = -9.897946, rho = -0.200629
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.671983
obj = -12.057635, rho = -0.215367
nSV = 70, nBSV = 63
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.569148
obj = -14.595200, rho = -0.176582
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.471198
obj = -17.763894, rho = -0.184792
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.398132
obj = -21.754079, rho = -0.236250
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.337122
obj = -26.724016, rho = -0.237300
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.283802
obj = -33.029752, rho = -0.199041
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.250033
obj = -41.038689, rho = -0.215949
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.213063
obj = -50.304116, rho = -0.169703
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.179490
obj = -62.321105, rho = -0.110819
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.158709
obj = -77.770361, rho = -0.150115
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.143762
obj = -95.069754, rho = -0.182772
nSV = 17, nBSV = 13
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.954146, rho = -0.921462
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.356185, rho = -0.887027
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.917066, rho = -0.837494
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.687794, rho = -0.766460
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.721809, rho = -0.664064
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -5.054755, rho = -0.516773
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 75% (75/100) (classification)
Accuracy = 74.1% (741/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.971652
obj = -6.653430, rho = -0.321095
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 96% (96/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.896721
obj = -8.533212, rho = -0.302460
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.817301
obj = -10.753280, rho = -0.265572
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.724990
obj = -13.285716, rho = -0.224720
nSV = 75, nBSV = 69
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.631425
obj = -16.208343, rho = -0.118691
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.533299
obj = -19.463031, rho = -0.122601
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.450345
obj = -23.234464, rho = -0.171379
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.368012
obj = -27.678326, rho = -0.155954
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.311845
obj = -33.084929, rho = -0.033818
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.256016
obj = -39.382509, rho = 0.014259
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.210588
obj = -46.745578, rho = -0.005772
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.172246
obj = -56.134598, rho = 0.095689
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*..*
optimization finished, #iter = 206
nu = 0.145146
obj = -66.979935, rho = 0.104785
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.120472
obj = -80.508125, rho = 0.133241
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.898007, rho = -0.943066
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.277869, rho = -0.918100
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.809450, rho = -0.882196
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.543419, rho = -0.830545
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.535707, rho = -0.756247
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.831699, rho = -0.649357
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 69% (69/100) (classification)
Accuracy = 65.9% (659/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.919111
obj = -6.424108, rho = -0.496772
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 87% (87/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.858390
obj = -8.321093, rho = -0.378842
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.789322
obj = -10.590077, rho = -0.289538
nSV = 82, nBSV = 77
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.704599
obj = -13.192672, rho = -0.213818
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.622247
obj = -16.241950, rho = -0.196377
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.534713
obj = -19.689547, rho = -0.197295
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.451489
obj = -23.656464, rho = -0.217344
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.371788
obj = -28.547712, rho = -0.277060
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.309620
obj = -34.755049, rho = -0.282293
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.267490
obj = -42.317149, rho = -0.350585
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.224645
obj = -51.088307, rho = -0.466098
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.195205
obj = -61.174528, rho = -0.515886
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.165210
obj = -72.092677, rho = -0.570044
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.135053
obj = -82.481774, rho = -0.671566
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.720000
obj = -0.709135, rho = 0.958393
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.720000
obj = -1.013202, rho = 0.940151
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.720000
obj = -1.443262, rho = 0.913910
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.720000
obj = -2.046721, rho = 0.876164
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.720000
obj = -2.883401, rho = 0.821868
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.720000
obj = -4.022022, rho = 0.743766
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.720000
obj = -5.525580, rho = 0.631420
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 64% (64/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.720000
obj = -7.410506, rho = 0.469816
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 75% (75/100) (classification)
Accuracy = 65.7% (657/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.720000
obj = -9.546935, rho = 0.237358
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 91.5% (915/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.660000
obj = -11.845703, rho = 0.109354
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.543845
obj = -14.430103, rho = 0.117997
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.486911
obj = -17.577532, rho = 0.016043
nSV = 51, nBSV = 48
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.412587
obj = -20.611719, rho = 0.079169
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.336506
obj = -24.096144, rho = 0.053360
nSV = 39, nBSV = 28
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.267187
obj = -28.389535, rho = 0.038430
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.224745
obj = -33.491404, rho = 0.129145
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.186016
obj = -39.245347, rho = 0.089430
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.151001
obj = -45.382580, rho = 0.081888
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*.*
optimization finished, #iter = 283
nu = 0.119880
obj = -52.451388, rho = -0.006077
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.098238
obj = -60.779267, rho = -0.089379
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.859835, rho = -0.948027
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.224111, rho = -0.925240
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.734507, rho = -0.892461
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.440551, rho = -0.845311
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -3.397945, rho = -0.777488
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -4.654654, rho = -0.679927
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 63% (63/100) (classification)
Accuracy = 56.6% (566/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.213129, rho = -0.539591
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 83% (83/100) (classification)
Accuracy = 81.9% (819/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -8.009284, rho = -0.455942
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 92% (92/100) (classification)
Accuracy = 91.7% (917/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.755746
obj = -10.096576, rho = -0.409287
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.681303
obj = -12.562191, rho = -0.324582
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.582094
obj = -15.463372, rho = -0.375406
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.504677
obj = -18.925614, rho = -0.266784
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.437403
obj = -22.942652, rho = -0.295029
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.362564
obj = -27.517158, rho = -0.322867
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.300934
obj = -33.308016, rho = -0.399916
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.252782
obj = -40.418044, rho = -0.436540
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.209142
obj = -49.528190, rho = -0.482961
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*..*
optimization finished, #iter = 437
nu = 0.173736
obj = -61.589963, rho = -0.513807
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.155282
obj = -77.840452, rho = -0.521503
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.139287
obj = -95.049761, rho = -0.440262
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.876332, rho = 0.868679
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.245633, rho = 0.811101
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.760895, rho = 0.728278
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.469053, rho = 0.609141
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.419377, rho = 0.437769
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.644996, rho = 0.191260
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 74% (74/100) (classification)
Accuracy = 67.4% (674/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.115464, rho = -0.163333
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 90% (90/100) (classification)
Accuracy = 88.1% (881/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -7.763362, rho = -0.291462
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.745762
obj = -9.701681, rho = -0.311646
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.646873
obj = -11.999637, rho = -0.296762
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.557332
obj = -14.817832, rho = -0.277643
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.476908
obj = -18.247915, rho = -0.228938
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 95% (95/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.406354
obj = -22.434781, rho = -0.180135
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 95% (95/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.342348
obj = -27.925571, rho = -0.194507
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 95% (95/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.297328
obj = -34.909438, rho = -0.220508
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.257061
obj = -44.081601, rho = -0.283407
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.224459
obj = -55.521456, rho = -0.280947
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.200602
obj = -69.885589, rho = -0.368251
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.178707
obj = -86.285668, rho = -0.390044
nSV = 23, nBSV = 12
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.154100
obj = -105.284745, rho = -0.462766
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -0.879287, rho = 0.899796
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -1.251748, rho = 0.855862
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -1.773547, rho = 0.792664
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -2.495230, rho = 0.701758
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -3.473541, rho = 0.570994
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -4.757069, rho = 0.382897
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 58% (58/100) (classification)
Accuracy = 57.8% (578/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -6.347359, rho = 0.112328
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 92% (92/100) (classification)
Accuracy = 87.6% (876/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.867899
obj = -8.135095, rho = -0.129545
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.789112
obj = -10.168552, rho = -0.227692
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.685529
obj = -12.503252, rho = -0.198872
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.583607
obj = -15.212715, rho = -0.163638
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.502055
obj = -18.498865, rho = -0.096262
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.429217
obj = -22.328439, rho = -0.109202
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.355415
obj = -26.759848, rho = -0.136183
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.292848
obj = -32.105375, rho = -0.158033
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.245714
obj = -38.812045, rho = -0.210658
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*....*
optimization finished, #iter = 526
nu = 0.206713
obj = -46.812900, rho = -0.254374
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.172346
obj = -56.631450, rho = -0.241205
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.144289
obj = -68.662839, rho = -0.264708
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.120836
obj = -83.921411, rho = -0.343140
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -0.840248, rho = 0.922615
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -1.196197, rho = 0.888685
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -1.694892, rho = 0.839879
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -2.384683, rho = 0.769674
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -3.319888, rho = 0.669048
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.860000
obj = -4.547149, rho = 0.523425
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -6.068368, rho = 0.314470
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 82% (82/100) (classification)
Accuracy = 78.9% (789/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -7.794208, rho = 0.071631
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -9.662540, rho = 0.036661
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.662922
obj = -11.737067, rho = 0.040501
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.549688
obj = -14.098962, rho = 0.011215
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.460967
obj = -17.007990, rho = 0.011460
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.382146
obj = -20.560664, rho = -0.024646
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.327816
obj = -24.938569, rho = -0.077492
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.279829
obj = -29.948867, rho = -0.146590
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.230628
obj = -35.691577, rho = -0.117648
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 217
nu = 0.197175
obj = -42.647896, rho = -0.078204
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.161662
obj = -50.019177, rho = -0.041125
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 382
nu = 0.134294
obj = -58.605851, rho = 0.035167
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.108391
obj = -68.070032, rho = 0.096982
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.840550, rho = 0.921203
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.196823, rho = 0.886654
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.696189, rho = 0.836958
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -2.387368, rho = 0.765474
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.860000
obj = -3.325448, rho = 0.662744
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.860000
obj = -4.558654, rho = 0.514978
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.860000
obj = -6.092177, rho = 0.302658
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 82% (82/100) (classification)
Accuracy = 76.6% (766/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.830288
obj = -7.826948, rho = 0.074984
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 92% (92/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.745871
obj = -9.791995, rho = -0.038922
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.671305
obj = -11.969644, rho = -0.080687
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.579655
obj = -14.376536, rho = -0.144288
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.475554
obj = -17.140327, rho = -0.212262
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.402545
obj = -20.352363, rho = -0.208200
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.329596
obj = -24.045414, rho = -0.211869
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.273464
obj = -28.432703, rho = -0.215854
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.217045
obj = -33.659966, rho = -0.209105
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.179923
obj = -40.321730, rho = -0.136886
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.153156
obj = -48.066597, rho = -0.060574
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 200
nu = 0.125876
obj = -56.537582, rho = 0.020324
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 229
nu = 0.101909
obj = -66.713933, rho = 0.048866
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.909347, rho = -0.940423
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.288719, rho = -0.914302
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.813756, rho = -0.876727
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.526230, rho = -0.822678
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.462597, rho = -0.744932
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 59% (59/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.626417, rho = -0.633097
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 79% (79/100) (classification)
Accuracy = 74.6% (746/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -5.985008, rho = -0.542399
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 91% (91/100) (classification)
Accuracy = 87% (870/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.824516
obj = -7.519102, rho = -0.475918
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 92.7% (927/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.717967
obj = -9.336760, rho = -0.443473
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 95% (95/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.625028
obj = -11.547659, rho = -0.367962
nSV = 64, nBSV = 62
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.545467
obj = -14.048479, rho = -0.349885
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.469897
obj = -16.808301, rho = -0.295111
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.391221
obj = -19.888094, rho = -0.351980
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.317868
obj = -23.599029, rho = -0.337085
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.267018
obj = -27.984334, rho = -0.501857
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.216733
obj = -32.886137, rho = -0.485766
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.177803
obj = -38.739287, rho = -0.467428
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.153373
obj = -45.385200, rho = -0.364113
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*..*
optimization finished, #iter = 294
nu = 0.120609
obj = -51.825759, rho = -0.313850
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.098246
obj = -59.695253, rho = -0.330069
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -0.878715, rho = -0.930825
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.250565, rho = -0.900719
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.771100, rho = -0.857189
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.490167, rho = -0.794574
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -3.463067, rho = -0.704304
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -4.735396, rho = -0.574655
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 59% (59/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.900000
obj = -6.302516, rho = -0.388328
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 86% (86/100) (classification)
Accuracy = 81.9% (819/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.862154
obj = -8.066884, rho = -0.215997
nSV = 90, nBSV = 85
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.792309
obj = -10.042680, rho = -0.078188
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.683044
obj = -12.220264, rho = -0.123203
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.576963
obj = -14.701452, rho = -0.183209
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.480052
obj = -17.733887, rho = -0.152040
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.400708
obj = -21.479836, rho = -0.224998
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.335851
obj = -26.178748, rho = -0.268514
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.288256
obj = -31.918579, rho = -0.322180
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.245498
obj = -38.492060, rho = -0.454159
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.205251
obj = -46.359318, rho = -0.377824
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.174457
obj = -55.106562, rho = -0.118703
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 189
nu = 0.142923
obj = -65.454199, rho = -0.099434
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.116018
obj = -78.528905, rho = -0.124300
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.931609, rho = 0.885946
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.322166, rho = 0.835940
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.864820, rho = 0.764007
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.605788, rho = 0.660537
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.589672, rho = 0.511699
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.835348, rho = 0.297603
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 74% (74/100) (classification)
Accuracy = 68.6% (686/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.944246
obj = -6.278781, rho = 0.043117
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.861092
obj = -7.853276, rho = -0.123479
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.763767
obj = -9.719193, rho = -0.043415
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.680000
obj = -11.722672, rho = -0.015003
nSV = 69, nBSV = 67
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.555149
obj = -13.870994, rho = 0.012579
nSV = 60, nBSV = 52
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.470272
obj = -16.475153, rho = -0.059940
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.382792
obj = -19.318895, rho = -0.059897
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.314639
obj = -22.608080, rho = -0.155594
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.251167
obj = -26.554312, rho = -0.145987
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.205489
obj = -31.316352, rho = -0.044427
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.167674
obj = -37.397223, rho = -0.153612
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.142217
obj = -44.328437, rho = -0.209130
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.113875
obj = -52.631129, rho = -0.226310
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.092319
obj = -63.463532, rho = -0.232252
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.878139, rho = 0.886237
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.249371, rho = 0.836358
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.768629, rho = 0.764609
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.485054, rho = 0.661402
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.452485, rho = 0.512944
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.713500, rho = 0.299395
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 68% (68/100) (classification)
Accuracy = 62.7% (627/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.257210, rho = -0.007786
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 85% (85/100) (classification)
Accuracy = 86% (860/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.846161
obj = -8.023138, rho = -0.170050
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 93% (93/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.761206
obj = -10.119247, rho = -0.144128
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 95% (95/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.680435
obj = -12.532445, rho = -0.148339
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.590846
obj = -15.343960, rho = -0.153091
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.499914
obj = -18.627934, rho = -0.165155
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.413941
obj = -22.856878, rho = -0.134167
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.364533
obj = -28.145919, rho = -0.135460
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.307810
obj = -34.124836, rho = -0.020525
nSV = 35, nBSV = 25
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.256808
obj = -41.801985, rho = 0.043358
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.216346
obj = -51.762786, rho = -0.030967
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.181265
obj = -65.112620, rho = -0.030122
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.160276
obj = -81.694441, rho = -0.153188
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.140232
obj = -103.326284, rho = -0.235766
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.921707, rho = -0.921331
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54.1% (541/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.314291, rho = -0.886839
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 54.1% (541/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.866671, rho = -0.837002
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 54.1% (541/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.635718, rho = -0.765536
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 54.1% (541/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.689144, rho = -0.662736
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 54.1% (541/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -5.095173, rho = -0.514862
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 54.7% (547/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.891579, rho = -0.302153
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 81% (81/100) (classification)
Accuracy = 81.5% (815/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -9.007794, rho = 0.003818
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -11.317922, rho = 0.067228
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.756977
obj = -13.967610, rho = -0.008955
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.651983
obj = -17.133220, rho = -0.046930
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.560784
obj = -20.828881, rho = -0.042132
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.463082
obj = -25.368123, rho = -0.061949
nSV = 50, nBSV = 42
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.395893
obj = -31.307808, rho = -0.073099
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.334970
obj = -38.692127, rho = 0.009466
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.285977
obj = -48.339291, rho = -0.016074
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.248988
obj = -60.277535, rho = -0.096393
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.220000
obj = -75.220956, rho = -0.030693
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.203482
obj = -89.777281, rho = 0.318545
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.165660
obj = -103.849689, rho = 0.270401
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -0.946696, rho = 0.844842
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.340770, rho = 0.776814
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.885169, rho = 0.678958
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.621793, rho = 0.538197
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.585245, rho = 0.335719
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 61% (61/100) (classification)
Accuracy = 60.2% (602/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.772185, rho = 0.044465
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 96% (96/100) (classification)
Accuracy = 93% (930/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.097257, rho = -0.184262
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.593161, rho = -0.111457
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.747372
obj = -9.296987, rho = -0.087745
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.642339
obj = -11.145625, rho = -0.075624
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.529036
obj = -13.293153, rho = -0.090507
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.446904
obj = -15.759212, rho = -0.046538
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.368674
obj = -18.480070, rho = -0.104999
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.308158
obj = -21.445625, rho = -0.198230
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.247661
obj = -24.527603, rho = -0.166356
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.194932
obj = -27.996427, rho = -0.175224
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.163078
obj = -31.827010, rho = -0.207631
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.125255
obj = -35.169029, rho = -0.173406
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.098518
obj = -38.475541, rho = -0.358385
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.074910
obj = -41.479893, rho = -0.378350
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.923391, rho = 0.840986
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.305162, rho = 0.771266
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.829636, rho = 0.670978
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.532988, rho = 0.526718
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.439039, rho = 0.319602
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 64% (64/100) (classification)
Accuracy = 60.5% (605/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.523669, rho = 0.021281
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 95% (95/100) (classification)
Accuracy = 87.7% (877/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.911824
obj = -5.684353, rho = -0.211836
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.785697
obj = -6.994371, rho = -0.152921
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.695793
obj = -8.492065, rho = -0.188253
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.586408
obj = -10.116764, rho = -0.157812
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.492215
obj = -11.930484, rho = -0.108528
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.400551
obj = -13.930662, rho = -0.133824
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.324026
obj = -16.404771, rho = -0.125326
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.270591
obj = -19.193475, rho = -0.112138
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.220000
obj = -22.300127, rho = -0.121231
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.178179
obj = -25.431935, rho = -0.199197
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.143903
obj = -28.996098, rho = -0.282875
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.112846
obj = -32.723080, rho = -0.345446
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.091317
obj = -36.793444, rho = -0.259933
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.073083
obj = -39.670496, rho = -0.218741
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.888649, rho = -0.929485
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.258504, rho = -0.898568
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.769382, rho = -0.854095
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.460512, rho = -0.790123
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.364162, rho = -0.698102
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 67% (67/100) (classification)
Accuracy = 56.4% (564/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.476745, rho = -0.565735
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 91% (91/100) (classification)
Accuracy = 79.5% (795/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.870909
obj = -5.754172, rho = -0.474084
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 89.4% (894/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.797093
obj = -7.238544, rho = -0.478077
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 93% (930/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.700268
obj = -8.935432, rho = -0.435623
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.613847
obj = -10.913789, rho = -0.397154
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.518915
obj = -13.155202, rho = -0.393553
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.441182
obj = -15.708658, rho = -0.393550
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.366297
obj = -18.545260, rho = -0.397402
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.301652
obj = -21.732308, rho = -0.388599
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.246409
obj = -25.326919, rho = -0.361321
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.201173
obj = -29.634491, rho = -0.425069
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.162794
obj = -34.650632, rho = -0.442466
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.137399
obj = -40.279360, rho = -0.637267
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.111800
obj = -45.577996, rho = -0.801611
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.084967
obj = -51.015070, rho = -0.822519
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.859375, rho = -0.936057
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.223161, rho = -0.908021
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.732540, rho = -0.867692
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.436482, rho = -0.809682
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -3.389525, rho = -0.726237
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -4.637233, rho = -0.606206
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 64% (64/100) (classification)
Accuracy = 57.5% (575/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.177083, rho = -0.433547
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 88% (88/100) (classification)
Accuracy = 86.5% (865/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.845374
obj = -7.903129, rho = -0.350908
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 92% (92/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.748751
obj = -9.858913, rho = -0.253795
nSV = 78, nBSV = 72
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.660000
obj = -12.271085, rho = -0.242667
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.563756
obj = -15.141500, rho = -0.215360
nSV = 60, nBSV = 52
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.489995
obj = -18.774883, rho = -0.269274
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.420896
obj = -23.167974, rho = -0.298419
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.355034
obj = -28.644520, rho = -0.327678
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.307413
obj = -35.722892, rho = -0.458680
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.274645
obj = -44.151859, rho = -0.317076
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.228850
obj = -54.128299, rho = -0.401193
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.197954
obj = -66.962699, rho = -0.458030
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.165393
obj = -82.686468, rho = -0.452817
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.138973
obj = -104.526136, rho = -0.464821
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700000
obj = -0.689458, rho = -0.972002
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.700000
obj = -0.985114, rho = -0.959859
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.700000
obj = -1.403289, rho = -0.942259
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 65% (65/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.700000
obj = -1.990113, rho = -0.916406
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 65% (65/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.700000
obj = -2.803814, rho = -0.879755
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 65% (65/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.700000
obj = -3.911348, rho = -0.827033
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 65% (65/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.700000
obj = -5.374268, rho = -0.751102
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 65% (65/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.700000
obj = -7.209161, rho = -0.641972
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 77% (77/100) (classification)
Accuracy = 66.7% (667/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.700000
obj = -9.291060, rho = -0.485340
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 94% (94/100) (classification)
Accuracy = 90% (900/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.626176
obj = -11.524084, rho = -0.397110
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.542994
obj = -14.014595, rho = -0.339841
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.461704
obj = -16.983800, rho = -0.344006
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.396853
obj = -20.358760, rho = -0.314745
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.324960
obj = -24.279395, rho = -0.299513
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.266399
obj = -29.030070, rho = -0.330626
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.232515
obj = -34.825119, rho = -0.218425
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.189875
obj = -40.692239, rho = -0.234182
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.152556
obj = -47.712176, rho = -0.169754
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.126091
obj = -55.933210, rho = -0.161121
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.105709
obj = -65.441348, rho = -0.134455
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.933565, rho = 0.877854
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.326213, rho = 0.824299
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.873194, rho = 0.747263
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.623115, rho = 0.636451
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.625524, rho = 0.477052
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 47.4% (474/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.909532, rho = 0.247766
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 78% (78/100) (classification)
Accuracy = 71.4% (714/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.948645
obj = -6.431903, rho = 0.012530
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 91% (91/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.171818, rho = -0.061005
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.786677
obj = -10.144333, rho = -0.022738
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.678427
obj = -12.355763, rho = 0.061387
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.577536
obj = -15.100644, rho = 0.025084
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.495254
obj = -18.353749, rho = 0.048534
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.434224
obj = -22.072751, rho = 0.067896
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.350755
obj = -26.141129, rho = 0.133872
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.286078
obj = -31.368611, rho = 0.114912
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.237917
obj = -38.088921, rho = 0.246318
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.203735
obj = -46.266057, rho = 0.187645
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.168797
obj = -56.032703, rho = 0.183755
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.144975
obj = -68.335330, rho = 0.252369
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.122484
obj = -82.154287, rho = 0.322776
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -0.822654, rho = 0.916137
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.172406, rho = 0.879367
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -1.663811, rho = 0.826476
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -2.346471, rho = 0.750395
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -3.278366, rho = 0.640955
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -4.515236, rho = 0.483532
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -6.080018, rho = 0.257087
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 80% (80/100) (classification)
Accuracy = 72.7% (727/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.832001
obj = -7.888723, rho = -0.037832
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.765328
obj = -9.910322, rho = -0.107536
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.664590
obj = -12.185986, rho = -0.097919
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.565151
obj = -14.964211, rho = -0.101341
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.483155
obj = -18.485766, rho = -0.108313
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.424545
obj = -22.540404, rho = -0.144427
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.354484
obj = -27.177112, rho = -0.083908
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.305438
obj = -32.805045, rho = 0.035479
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.255558
obj = -39.207220, rho = 0.031729
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.205980
obj = -46.906911, rho = -0.015116
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.173051
obj = -57.092746, rho = -0.092546
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.152592
obj = -68.577301, rho = 0.010443
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.124173
obj = -80.618710, rho = -0.015219
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.967774, rho = -0.056928
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.371770, rho = -0.081888
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.931168, rho = -0.117792
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.690872, rho = -0.169438
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.690636, rho = -0.243728
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.943881, rho = -0.326607
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.942472
obj = -6.465618, rho = -0.269081
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 92% (92/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.306407, rho = -0.353711
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.783731
obj = -10.481062, rho = -0.355778
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.687351
obj = -13.161585, rho = -0.333300
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.607643
obj = -16.479927, rho = -0.282989
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.531732
obj = -20.460087, rho = -0.243017
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.455311
obj = -25.290843, rho = -0.258902
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.384843
obj = -31.365282, rho = -0.276114
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.339915
obj = -39.120213, rho = -0.413177
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.288105
obj = -48.644728, rho = -0.526887
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.249028
obj = -60.882124, rho = -0.477567
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.211091
obj = -77.369915, rho = -0.519731
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.184926
obj = -99.764169, rho = -0.485356
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 284
nu = 0.167463
obj = -128.740363, rho = -0.372334
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -0.858448, rho = -0.949779
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.221241, rho = -0.927760
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -1.728573, rho = -0.895858
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -2.428274, rho = -0.850045
nSV = 91, nBSV = 86
Total nSV = 91
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.880000
obj = -3.372545, rho = -0.784518
nSV = 91, nBSV = 86
Total nSV = 91
Accuracy = 56% (56/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.880000
obj = -4.602104, rho = -0.689608
nSV = 91, nBSV = 86
Total nSV = 91
Accuracy = 61% (61/100) (classification)
Accuracy = 54.7% (547/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.880000
obj = -6.104397, rho = -0.553517
nSV = 91, nBSV = 86
Total nSV = 91
Accuracy = 83% (83/100) (classification)
Accuracy = 81% (810/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -7.771825, rho = -0.418494
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.771073
obj = -9.635816, rho = -0.325116
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.658963
obj = -11.616984, rho = -0.274339
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.551001
obj = -13.930388, rho = -0.244865
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.466154
obj = -16.592465, rho = -0.186838
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.386129
obj = -19.510557, rho = -0.145327
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.316049
obj = -23.081407, rho = -0.192810
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.266397
obj = -26.748902, rho = -0.166021
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.214047
obj = -30.864116, rho = -0.137527
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.175277
obj = -35.513604, rho = -0.168421
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.143427
obj = -39.619541, rho = -0.160021
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.106876
obj = -43.657514, rho = -0.209408
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.083825
obj = -48.062307, rho = -0.293452
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.877944, rho = 0.913437
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.248968, rho = 0.875484
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.767795, rho = 0.820890
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.483329, rho = 0.742359
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.448916, rho = 0.629397
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.706117, rho = 0.466906
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 57% (57/100) (classification)
Accuracy = 56.7% (567/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.241933, rho = 0.233171
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 90% (90/100) (classification)
Accuracy = 87.4% (874/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.871326
obj = -7.925191, rho = 0.051872
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.779581
obj = -9.706699, rho = -0.059099
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.660000
obj = -11.727596, rho = -0.135016
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.551869
obj = -14.137769, rho = -0.167644
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.458873
obj = -17.148928, rho = -0.181445
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.384875
obj = -20.859847, rho = -0.160285
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.329788
obj = -25.431358, rho = -0.131140
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.283737
obj = -30.584924, rho = -0.155869
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.238330
obj = -36.409689, rho = -0.043323
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.196807
obj = -43.179961, rho = 0.019827
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.161400
obj = -50.939882, rho = 0.028668
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.132640
obj = -60.202772, rho = -0.047224
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.105038
obj = -72.516315, rho = -0.069487
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.929052, rho = 0.847848
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.316877, rho = 0.781137
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -1.853878, rho = 0.684947
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -2.583147, rho = 0.546811
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.960000
obj = -3.542828, rho = 0.347472
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 55% (55/100) (classification)
Accuracy = 55.2% (552/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -4.738422, rho = 0.061124
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 81% (81/100) (classification)
Accuracy = 79.4% (794/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.937455
obj = -6.081265, rho = -0.272126
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840185
obj = -7.618295, rho = -0.309158
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.730943
obj = -9.412409, rho = -0.280440
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.640000
obj = -11.581840, rho = -0.348779
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.554707
obj = -13.962984, rho = -0.290087
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.464189
obj = -16.689439, rho = -0.305376
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.382354
obj = -19.936272, rho = -0.359113
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.313906
obj = -23.977842, rho = -0.431172
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 21
nu = 0.263997
obj = -29.172655, rho = -0.433123
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.224672
obj = -34.869478, rho = -0.502478
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.191131
obj = -41.578895, rho = -0.637379
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.159808
obj = -48.090134, rho = -0.647929
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 326
nu = 0.129348
obj = -55.031972, rho = -0.663314
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.106541
obj = -61.939145, rho = -0.667501
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.969128, rho = -0.023049
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.374572, rho = -0.033155
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.936966, rho = -0.047692
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.702870, rho = -0.068603
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.715461, rho = -0.098682
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.987617, rho = -0.141949
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.976124
obj = -6.458560, rho = -0.119273
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.898689
obj = -8.115239, rho = -0.109854
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.787022
obj = -9.995512, rho = -0.113217
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.676630
obj = -12.198717, rho = -0.071080
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.579632
obj = -14.736133, rho = 0.017761
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.494710
obj = -17.586179, rho = -0.058167
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.410996
obj = -20.822714, rho = -0.037577
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.335474
obj = -24.574985, rho = -0.005319
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.275177
obj = -29.169264, rho = -0.055049
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.227771
obj = -34.827850, rho = -0.084427
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.190354
obj = -41.362381, rho = -0.075064
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 206
nu = 0.154710
obj = -48.751994, rho = -0.037327
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.125996
obj = -58.093571, rho = -0.131712
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.105224
obj = -69.346354, rho = -0.173786
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -0.786599, rho = 0.945396
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -1.123031, rho = 0.921455
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -1.597935, rho = 0.887017
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -2.262362, rho = 0.838026
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -3.179420, rho = 0.767009
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.800000
obj = -4.418517, rho = 0.665053
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -6.035253, rho = 0.517718
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 65% (65/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.800000
obj = -8.018132, rho = 0.306948
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 85% (85/100) (classification)
Accuracy = 84.6% (846/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760655
obj = -10.229768, rho = 0.119648
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.688291
obj = -12.816357, rho = 0.097158
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.599476
obj = -15.739104, rho = 0.059057
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.510657
obj = -19.326612, rho = 0.111700
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.448178
obj = -23.450596, rho = 0.070298
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.370625
obj = -28.172743, rho = 0.067900
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.310632
obj = -33.990439, rho = 0.043817
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.264027
obj = -40.641499, rho = 0.238482
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.223353
obj = -47.762123, rho = 0.371374
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.182983
obj = -55.568967, rho = 0.385540
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.146044
obj = -64.800214, rho = 0.353192
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.120055
obj = -75.777952, rho = 0.285964
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.892213, rho = 0.891683
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.265878, rho = 0.844192
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.784641, rho = 0.775877
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.492086, rho = 0.677611
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.429491, rho = 0.536259
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.611920, rho = 0.332932
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 82% (82/100) (classification)
Accuracy = 70.7% (707/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -5.969345, rho = 0.040457
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -7.394936, rho = 0.041873
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.723120
obj = -8.954638, rho = -0.005573
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.604339
obj = -10.821049, rho = 0.013599
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.507226
obj = -13.067870, rho = -0.026498
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.432950
obj = -15.789333, rho = -0.032984
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.363585
obj = -18.916546, rho = -0.035559
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.302102
obj = -22.710988, rho = -0.026076
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.252481
obj = -27.116880, rho = -0.008442
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.207700
obj = -32.569263, rho = 0.005722
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.180072
obj = -38.919374, rho = -0.068518
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.146783
obj = -45.465488, rho = -0.056534
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.119035
obj = -53.335624, rho = 0.030150
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.097641
obj = -62.854737, rho = -0.011771
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -0.764497, rho = 0.937091
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 46.6% (466/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -1.089912, rho = 0.909509
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 46.6% (466/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -1.547552, rho = 0.869833
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 46.6% (466/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -2.184214, rho = 0.812761
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 46.6% (466/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -3.055264, rho = 0.730666
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 46.6% (466/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -4.215618, rho = 0.612577
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 61% (61/100) (classification)
Accuracy = 46.7% (467/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -5.693112, rho = 0.442712
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 73% (73/100) (classification)
Accuracy = 60.2% (602/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -7.421929, rho = 0.198369
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 93% (93/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.720731
obj = -9.234848, rho = 0.069486
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.619858
obj = -11.314778, rho = 0.000923
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.539472
obj = -13.794581, rho = -0.023902
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.452405
obj = -16.593016, rho = -0.019736
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.381228
obj = -19.962714, rho = -0.056113
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.320985
obj = -23.797333, rho = -0.044679
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.270068
obj = -28.056182, rho = -0.099654
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*..*
optimization finished, #iter = 224
nu = 0.218755
obj = -32.977592, rho = -0.073704
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.177303
obj = -39.106079, rho = -0.071303
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.**.*
optimization finished, #iter = 187
nu = 0.145899
obj = -46.317290, rho = -0.067156
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.119872
obj = -55.535007, rho = -0.141309
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.098160
obj = -67.255516, rho = -0.192154
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -0.783461, rho = -0.960214
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -1.116538, rho = -0.942770
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -1.584501, rho = -0.917677
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -2.234566, rho = -0.881583
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -3.121907, rho = -0.829459
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -4.299511, rho = -0.755450
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 60% (60/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -5.789016, rho = -0.648227
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 77% (77/100) (classification)
Accuracy = 62.1% (621/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -7.508629, rho = -0.494382
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 92% (92/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.715061
obj = -9.352933, rho = -0.431840
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.637946
obj = -11.420152, rho = -0.382161
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.549898
obj = -13.793546, rho = -0.297636
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.462367
obj = -16.417107, rho = -0.253691
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.374512
obj = -19.572050, rho = -0.296559
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.314222
obj = -23.415275, rho = -0.335494
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.264513
obj = -27.930620, rho = -0.261252
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.218151
obj = -33.179840, rho = -0.288333
nSV = 24, nBSV = 20
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.179388
obj = -39.207722, rho = -0.318796
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.148458
obj = -46.546422, rho = -0.327304
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.123817
obj = -54.477484, rho = -0.367254
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.098324
obj = -63.316242, rho = -0.517274
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.874240, rho = -0.935161
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.241305, rho = -0.906733
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.751941, rho = -0.865275
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.450525, rho = -0.806205
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.381039, rho = -0.721236
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.565670, rho = -0.599011
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 77% (77/100) (classification)
Accuracy = 67.3% (673/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -5.951328, rho = -0.423198
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.487255, rho = -0.402000
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.730416
obj = -9.248867, rho = -0.346664
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.615028
obj = -11.354249, rho = -0.341366
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.532419
obj = -13.936220, rho = -0.312048
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.455868
obj = -16.913035, rho = -0.241063
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.395366
obj = -20.386389, rho = -0.202053
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.323927
obj = -24.326131, rho = -0.240142
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.268229
obj = -28.991768, rho = -0.198997
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.221128
obj = -34.849359, rho = -0.179731
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.190630
obj = -41.771212, rho = -0.011187
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.154737
obj = -49.475308, rho = 0.001552
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.127451
obj = -59.419988, rho = -0.042704
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.107877
obj = -71.152717, rho = -0.031590
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.897000, rho = 0.885054
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.275784, rho = 0.834656
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.805136, rho = 0.762161
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.534493, rho = 0.657881
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.517239, rho = 0.507878
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.793481, rho = 0.292108
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 64% (64/100) (classification)
Accuracy = 62.1% (621/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -6.345019, rho = -0.018267
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 89% (89/100) (classification)
Accuracy = 88% (880/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.867588
obj = -8.090647, rho = -0.174205
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.785024
obj = -10.057786, rho = -0.213549
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.673383
obj = -12.322702, rho = -0.185089
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.581027
obj = -15.066303, rho = -0.262889
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.504260
obj = -18.149861, rho = -0.259840
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.419040
obj = -21.493750, rho = -0.215053
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.344229
obj = -25.547661, rho = -0.280840
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.289266
obj = -30.329458, rho = -0.310089
nSV = 31, nBSV = 28
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.244015
obj = -35.310493, rho = -0.307216
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.193465
obj = -40.741649, rho = -0.390747
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.153845
obj = -47.592035, rho = -0.322663
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.124358
obj = -56.142492, rho = -0.274675
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.112763
obj = -63.985481, rho = -0.140317
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.914370, rho = -0.918737
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.299111, rho = -0.883107
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.835260, rho = -0.831856
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.570724, rho = -0.758133
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.554662, rho = -0.652086
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 52.2% (522/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.816912, rho = -0.499543
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 72% (72/100) (classification)
Accuracy = 69.3% (693/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.315820, rho = -0.280118
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.869076
obj = -7.955538, rho = -0.201014
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.779665
obj = -9.772848, rho = -0.148651
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.680000
obj = -11.853046, rho = -0.066412
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.559039
obj = -14.181061, rho = -0.027062
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.465397
obj = -17.014928, rho = 0.044692
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.391398
obj = -20.356119, rho = 0.058505
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.325255
obj = -24.253129, rho = 0.084407
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.275021
obj = -28.917257, rho = 0.142733
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.225706
obj = -34.247350, rho = 0.102759
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.185659
obj = -40.198080, rho = 0.256933
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.156488
obj = -47.056459, rho = 0.184187
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.124449
obj = -54.456531, rho = 0.160255
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.103585
obj = -61.701940, rho = 0.147083
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.951289, rho = 0.842926
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.350275, rho = 0.774057
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.904836, rho = 0.674993
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.662487, rho = 0.532493
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.669446, rho = 0.327515
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 54% (54/100) (classification)
Accuracy = 55.6% (556/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.946407, rho = 0.032664
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 83% (83/100) (classification)
Accuracy = 82.3% (823/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.951637
obj = -6.446000, rho = -0.225082
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 93% (93/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -8.218495, rho = -0.247278
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.792812
obj = -10.276024, rho = -0.162610
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.694018
obj = -12.653943, rho = -0.223971
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.597171
obj = -15.379212, rho = -0.146945
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.501458
obj = -18.639964, rho = -0.150360
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.422607
obj = -22.713377, rho = -0.114722
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.359916
obj = -27.342722, rho = -0.053940
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.301924
obj = -33.135561, rho = -0.055611
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.257003
obj = -39.718838, rho = -0.044333
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.212462
obj = -47.550392, rho = 0.016840
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.175973
obj = -56.858402, rho = 0.046052
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.143131
obj = -68.914778, rho = 0.057679
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.125430
obj = -83.798601, rho = 0.038539
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.896344, rho = -0.934518
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.274426, rho = -0.905807
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.802328, rho = -0.864509
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.528682, rho = -0.805103
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.505214, rho = -0.719650
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 48.4% (484/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.768602, rho = -0.596731
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 67% (67/100) (classification)
Accuracy = 59.2% (592/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.293540, rho = -0.419917
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 89% (89/100) (classification)
Accuracy = 85% (850/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.855886
obj = -7.987372, rho = -0.319838
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 91.9% (919/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.760000
obj = -10.005788, rho = -0.264245
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.671181
obj = -12.359133, rho = -0.184961
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.586279
obj = -15.098168, rho = -0.136589
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.483822
obj = -18.414070, rho = -0.130331
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.407368
obj = -22.662354, rho = -0.100246
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 22
nu = 0.357706
obj = -27.860689, rho = -0.060179
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.310773
obj = -33.733112, rho = -0.119038
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.266983
obj = -40.054881, rho = -0.041247
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.215289
obj = -47.147160, rho = -0.062826
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.174549
obj = -56.269124, rho = -0.052730
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 202
nu = 0.144084
obj = -67.858904, rho = 0.069382
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.119212
obj = -83.003763, rho = 0.024935
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.896747, rho = 0.881306
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.275260, rho = 0.829265
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -1.804055, rho = 0.755319
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.532256, rho = 0.648038
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.512609, rho = 0.493721
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.783902, rho = 0.271743
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 73% (73/100) (classification)
Accuracy = 67.1% (671/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.325199, rho = -0.047562
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 92% (92/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.856978
obj = -8.093694, rho = -0.113519
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 95% (95/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.776412
obj = -10.155654, rho = -0.138969
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.678274
obj = -12.608872, rho = -0.109657
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.590071
obj = -15.519900, rho = -0.036339
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.505790
obj = -18.795891, rho = -0.083540
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.421517
obj = -22.872569, rho = -0.077520
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.359618
obj = -28.002113, rho = -0.116675
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.310623
obj = -33.908054, rho = -0.169958
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.257460
obj = -40.766237, rho = -0.212895
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.221359
obj = -48.700853, rho = -0.184698
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.178629
obj = -57.984559, rho = -0.177519
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.148386
obj = -70.514873, rho = -0.135122
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 229
nu = 0.129524
obj = -83.711479, rho = -0.092493
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.951226, rho = -0.904730
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.350144, rho = -0.862960
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.904565, rho = -0.802874
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.661927, rho = -0.716444
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.668287, rho = -0.592119
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 58% (58/100) (classification)
Accuracy = 55.8% (558/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.944011, rho = -0.413284
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 90% (90/100) (classification)
Accuracy = 84.8% (848/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.950959
obj = -6.442380, rho = -0.265730
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.865667
obj = -8.221322, rho = -0.254980
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.774368
obj = -10.369561, rho = -0.188438
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.689182
obj = -12.951666, rho = -0.139559
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.601499
obj = -15.994403, rho = -0.179380
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.533827
obj = -19.392204, rho = -0.198296
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.445572
obj = -23.141394, rho = -0.187304
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.372384
obj = -27.494071, rho = -0.220810
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.312261
obj = -32.572814, rho = -0.166668
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.255475
obj = -37.935088, rho = -0.144142
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.209029
obj = -44.320557, rho = -0.102096
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 194
nu = 0.170441
obj = -51.372480, rho = -0.106546
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.137470
obj = -59.703293, rho = -0.116062
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.109559
obj = -68.706711, rho = -0.138247
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.954229, rho = -0.918962
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.356358, rho = -0.883431
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.917422, rho = -0.832322
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.688530, rho = -0.758804
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.723332, rho = -0.653051
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.057907, rho = -0.500931
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 79% (79/100) (classification)
Accuracy = 79% (790/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.969151
obj = -6.660939, rho = -0.299678
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 92% (92/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.904026
obj = -8.524274, rho = -0.236990
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.820000
obj = -10.679514, rho = -0.149400
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.709423
obj = -13.216838, rho = -0.098792
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.621813
obj = -16.152582, rho = -0.086414
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.520365
obj = -19.739117, rho = -0.076144
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.442805
obj = -24.122996, rho = -0.059413
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.379303
obj = -29.608744, rho = -0.082659
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.323837
obj = -36.340126, rho = -0.067692
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.280000
obj = -44.252608, rho = 0.015317
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.231318
obj = -53.735898, rho = -0.017816
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.197011
obj = -65.486153, rho = 0.054016
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.169157
obj = -79.738241, rho = 0.157636
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.146938
obj = -95.734274, rho = 0.304086
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -0.839953, rho = 0.922676
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.195587, rho = 0.888774
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -1.693633, rho = 0.840543
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.860000
obj = -2.382084, rho = 0.770421
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.860000
obj = -3.314519, rho = 0.669782
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.860000
obj = -4.536037, rho = 0.524998
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 58% (58/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.860000
obj = -6.045378, rho = 0.317325
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 77% (77/100) (classification)
Accuracy = 73.7% (737/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.840000
obj = -7.712491, rho = 0.063028
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 94% (94/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.737534
obj = -9.543550, rho = 0.031758
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.642570
obj = -11.686600, rho = -0.012589
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.555717
obj = -14.227390, rho = 0.005756
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.466424
obj = -17.088294, rho = -0.037139
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.404800
obj = -20.283679, rho = -0.193976
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.322040
obj = -23.890357, rho = -0.219624
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.265679
obj = -28.326993, rho = -0.147590
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.226614
obj = -33.473641, rho = -0.135774
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.184932
obj = -39.188460, rho = -0.076940
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.152121
obj = -45.043894, rho = -0.079975
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.121342
obj = -50.955906, rho = -0.108023
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.095014
obj = -57.946328, rho = -0.146339
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.925886, rho = 0.793748
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.310325, rho = 0.703317
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.840319, rho = 0.573236
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.555092, rho = 0.386122
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.484774, rho = 0.116967
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 73% (73/100) (classification)
Accuracy = 66.6% (666/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.618300, rho = -0.270198
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 88% (88/100) (classification)
Accuracy = 86.6% (866/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -5.933115, rho = -0.355254
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 92% (92/100) (classification)
Accuracy = 88.9% (889/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.808648
obj = -7.493431, rho = -0.364928
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.704442
obj = -9.395181, rho = -0.327072
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.629834
obj = -11.705655, rho = -0.321291
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.550145
obj = -14.431327, rho = -0.330356
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.485202
obj = -17.539002, rho = -0.292377
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.412168
obj = -20.856596, rho = -0.233861
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.345345
obj = -24.288038, rho = -0.197256
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.278998
obj = -27.844438, rho = -0.126659
nSV = 34, nBSV = 23
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.224852
obj = -31.927184, rho = -0.115241
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.179219
obj = -36.097031, rho = -0.226269
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.144098
obj = -40.229596, rho = -0.299066
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.111229
obj = -44.510097, rho = -0.360754
nSV = 18, nBSV = 6
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.085842
obj = -49.099547, rho = -0.408957
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -0.947680, rho = 0.825920
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.342806, rho = 0.749595
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.889381, rho = 0.639805
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -2.630509, rho = 0.481877
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -3.603280, rho = 0.254706
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 60% (60/100) (classification)
Accuracy = 62.7% (627/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.809502, rho = -0.072068
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 90% (90/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.945351
obj = -6.174702, rho = -0.271670
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.853008
obj = -7.731459, rho = -0.231867
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.750597
obj = -9.533596, rho = -0.174481
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.659295
obj = -11.554886, rho = -0.142928
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.545092
obj = -13.849502, rho = -0.147890
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.458811
obj = -16.509525, rho = -0.199764
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.376532
obj = -19.712341, rho = -0.179980
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.309071
obj = -23.706429, rho = -0.147436
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.263988
obj = -28.471021, rho = -0.161116
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.219026
obj = -33.854568, rho = -0.139837
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.177634
obj = -40.825461, rho = -0.145029
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.151478
obj = -49.511458, rho = -0.151070
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.127614
obj = -60.113458, rho = -0.151651
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.105974
obj = -73.329959, rho = -0.154140
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.837805, rho = 0.878628
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.191142, rho = 0.825412
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.684433, rho = 0.748864
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.363041, rho = 0.638753
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.275109, rho = 0.480365
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.454493, rho = 0.252531
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 71% (71/100) (classification)
Accuracy = 65.2% (652/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -5.876651, rho = -0.075197
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 93% (93/100) (classification)
Accuracy = 85.7% (857/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -7.509672, rho = -0.196439
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.726078
obj = -9.315600, rho = -0.255407
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.640000
obj = -11.370373, rho = -0.223061
nSV = 64, nBSV = 64
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.547059
obj = -13.563816, rho = -0.212819
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.459919
obj = -15.874399, rho = -0.276340
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.380000
obj = -18.353478, rho = -0.274304
nSV = 40, nBSV = 37
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.304227
obj = -20.818752, rho = -0.306949
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.241119
obj = -23.569689, rho = -0.333743
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.198612
obj = -26.278318, rho = -0.335238
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.150555
obj = -28.427429, rho = -0.362723
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.113088
obj = -30.983959, rho = -0.346373
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.085051
obj = -33.825644, rho = -0.456577
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.066361
obj = -37.045869, rho = -0.544129
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -0.804467, rho = -0.941459
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.147390, rho = -0.915791
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.630193, rho = -0.878870
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -2.303011, rho = -0.825760
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -3.225985, rho = -0.749365
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -4.460855, rho = -0.639474
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -6.045178, rho = -0.481401
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 74% (74/100) (classification)
Accuracy = 65.1% (651/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -7.926921, rho = -0.254021
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 93% (93/100) (classification)
Accuracy = 91.5% (915/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.766628
obj = -9.928417, rho = -0.092213
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.678557
obj = -12.164978, rho = -0.147066
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.569977
obj = -14.776576, rho = -0.095267
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.487681
obj = -17.845784, rho = -0.074233
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.398919
obj = -21.594679, rho = -0.065312
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.333072
obj = -26.535998, rho = -0.002704
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.286489
obj = -32.685440, rho = -0.021809
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.245930
obj = -40.320095, rho = -0.060264
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.210728
obj = -49.631780, rho = -0.110782
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.181378
obj = -61.463689, rho = -0.020880
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.161155
obj = -74.722472, rho = 0.027935
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.134637
obj = -90.035366, rho = 0.134763
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -0.822159, rho = 0.926966
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -1.171383, rho = 0.895689
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -1.661694, rho = 0.849954
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -2.342094, rho = 0.785894
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -3.269309, rho = 0.692020
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.840000
obj = -4.496497, rho = 0.556568
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 58% (58/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.840000
obj = -6.041244, rho = 0.362145
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 74% (74/100) (classification)
Accuracy = 68.6% (686/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.830004
obj = -7.809266, rho = 0.120682
nSV = 85, nBSV = 80
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 91.9% (919/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.765706
obj = -9.663952, rho = 0.023215
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.653594
obj = -11.751417, rho = 0.019530
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.561336
obj = -14.120758, rho = 0.072829
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.469805
obj = -16.867423, rho = 0.005810
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.389037
obj = -20.136575, rho = 0.041753
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.324907
obj = -23.941001, rho = -0.021442
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.268947
obj = -28.339830, rho = 0.047834
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.216624
obj = -33.667007, rho = 0.022947
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.180191
obj = -40.314533, rho = 0.096286
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.149622
obj = -48.581635, rho = 0.192744
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 226
nu = 0.126695
obj = -58.203874, rho = 0.164333
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.105003
obj = -69.573278, rho = 0.014769
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.945415, rho = -0.903727
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.338120, rho = -0.861516
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.879686, rho = -0.800798
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.610448, rho = -0.713458
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.561771, rho = -0.587824
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 63% (63/100) (classification)
Accuracy = 60.1% (601/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.723615, rho = -0.407106
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 92% (92/100) (classification)
Accuracy = 84.9% (849/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.926660
obj = -6.020915, rho = -0.300497
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 98% (98/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.827399
obj = -7.526309, rho = -0.340925
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.737639
obj = -9.256615, rho = -0.261637
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.623546
obj = -11.267618, rho = -0.280257
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.540000
obj = -13.691828, rho = -0.293487
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.449101
obj = -16.564422, rho = -0.265718
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.374058
obj = -20.006273, rho = -0.287019
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.318272
obj = -24.349308, rho = -0.288918
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.269198
obj = -29.502946, rho = -0.274803
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.225270
obj = -35.456047, rho = -0.303575
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.182709
obj = -43.242017, rho = -0.339854
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.154823
obj = -53.420218, rho = -0.405269
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.138028
obj = -65.265906, rho = -0.496629
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.113012
obj = -79.643929, rho = -0.553641
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.946310, rho = 0.837151
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.339973, rho = 0.765750
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.883519, rho = 0.663043
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.618380, rho = 0.515305
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.5% (485/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.578184, rho = 0.302790
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 66% (66/100) (classification)
Accuracy = 62.2% (622/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.757574, rho = -0.002901
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 97% (97/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.923249
obj = -6.086091, rho = -0.120137
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.840000
obj = -7.655315, rho = -0.141317
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -9.493188, rho = -0.171007
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.650577
obj = -11.577622, rho = -0.071383
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.560934
obj = -13.882364, rho = -0.079068
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.465820
obj = -16.319680, rho = -0.029418
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.388959
obj = -19.062526, rho = -0.010286
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.317596
obj = -21.848948, rho = 0.000086
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.253543
obj = -24.898899, rho = 0.012584
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.198385
obj = -28.381006, rho = -0.019751
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.159897
obj = -32.270621, rho = -0.028311
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.125090
obj = -36.514446, rho = 0.018504
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.098335
obj = -41.222098, rho = 0.011599
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.079830
obj = -45.956616, rho = 0.013062
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.943727, rho = 0.800533
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.334626, rho = 0.713077
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.872457, rho = 0.587276
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.1% (491/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.595490, rho = 0.406317
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.530821, rho = 0.146016
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 76% (76/100) (classification)
Accuracy = 71.4% (714/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.659574, rho = -0.228413
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 97% (97/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.914442
obj = -5.941515, rho = -0.312157
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.813758
obj = -7.458128, rho = -0.325787
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.720000
obj = -9.244618, rho = -0.280620
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.621999
obj = -11.339248, rho = -0.239469
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.531691
obj = -13.769665, rho = -0.190655
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.449813
obj = -16.755658, rho = -0.201330
nSV = 46, nBSV = 44
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.391933
obj = -20.045648, rho = -0.068258
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.316576
obj = -23.960409, rho = -0.092009
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.268329
obj = -28.591086, rho = -0.104587
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.220685
obj = -34.083835, rho = -0.048515
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.179711
obj = -40.952675, rho = -0.046650
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.153444
obj = -48.867271, rho = -0.077656
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.126863
obj = -58.668706, rho = -0.032082
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.110601
obj = -69.227006, rho = -0.045594
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -0.819826, rho = -0.947792
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -1.166557, rho = -0.925260
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -1.651708, rho = -0.892490
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -2.321427, rho = -0.845353
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -3.226548, rho = -0.777548
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -4.408017, rho = -0.680013
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 61% (61/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -5.858168, rho = -0.539715
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 82% (82/100) (classification)
Accuracy = 76.2% (762/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -7.471871, rho = -0.428598
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.731559
obj = -9.253798, rho = -0.412884
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.639660
obj = -11.110873, rho = -0.296555
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.538915
obj = -13.207340, rho = -0.245707
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.443778
obj = -15.497518, rho = -0.241360
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.363643
obj = -18.056173, rho = -0.171420
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.292084
obj = -21.144301, rho = -0.161407
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.239819
obj = -24.562440, rho = -0.183269
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.196120
obj = -28.501230, rho = -0.160459
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.159847
obj = -32.431277, rho = -0.157527
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.127794
obj = -36.726409, rho = -0.134092
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.102623
obj = -40.537545, rho = -0.151417
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.081482
obj = -43.137068, rho = -0.127889
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.954418, rho = 0.860760
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.356749, rho = 0.799711
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.918231, rho = 0.711894
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.690204, rho = 0.585574
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.726797, rho = 0.403869
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 53% (53/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.065075, rho = 0.142495
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 75% (75/100) (classification)
Accuracy = 77.1% (771/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.966182
obj = -6.677066, rho = -0.134124
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 92% (92/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.910335
obj = -8.567563, rho = -0.237138
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 95% (95/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -10.763448, rho = -0.158067
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.720000
obj = -13.318520, rho = -0.139616
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.630419
obj = -16.321243, rho = -0.084261
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.525565
obj = -19.840070, rho = -0.113985
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.446291
obj = -24.235853, rho = -0.163980
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.385192
obj = -29.429120, rho = -0.258800
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.323254
obj = -35.524872, rho = -0.117760
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.266712
obj = -43.193676, rho = -0.119612
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.233996
obj = -52.341515, rho = -0.235923
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.196746
obj = -62.797531, rho = -0.298941
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.160004
obj = -75.698331, rho = -0.295791
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 293
nu = 0.133159
obj = -92.710535, rho = -0.350881
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.933138, rho = 0.867357
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.325332, rho = 0.809200
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.871369, rho = 0.725544
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.619339, rho = 0.605209
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.617711, rho = 0.432113
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 54% (54/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.893366, rho = 0.183123
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 72% (72/100) (classification)
Accuracy = 74.8% (748/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.947669
obj = -6.398219, rho = -0.108571
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.865957
obj = -8.141594, rho = -0.125636
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.780322
obj = -10.170390, rho = -0.126013
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.692007
obj = -12.456955, rho = -0.167105
nSV = 73, nBSV = 67
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.590470
obj = -15.108711, rho = -0.152542
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.504269
obj = -17.995599, rho = -0.198384
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.412669
obj = -21.338208, rho = -0.176157
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.339544
obj = -25.501136, rho = -0.201593
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.280088
obj = -30.632265, rho = -0.193287
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.236288
obj = -36.839902, rho = -0.191291
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.205645
obj = -43.467870, rho = -0.242047
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.169465
obj = -50.363633, rho = -0.381301
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*...*
optimization finished, #iter = 496
nu = 0.133367
obj = -57.654860, rho = -0.427025
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.105336
obj = -67.440255, rho = -0.436985
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.720000
obj = -0.708293, rho = -0.965894
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.720000
obj = -1.011461, rho = -0.950941
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.720000
obj = -1.439660, rho = -0.928673
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.720000
obj = -2.039276, rho = -0.897641
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.720000
obj = -2.867997, rho = -0.852762
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.720000
obj = -3.990156, rho = -0.788462
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.720000
obj = -5.459649, rho = -0.695919
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 64% (64/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.720000
obj = -7.274090, rho = -0.563342
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 84% (84/100) (classification)
Accuracy = 72.5% (725/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.720000
obj = -9.264674, rho = -0.370966
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.651900
obj = -11.204326, rho = -0.298290
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.539706
obj = -13.232930, rho = -0.315636
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.442982
obj = -15.619399, rho = -0.333383
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.363310
obj = -18.440438, rho = -0.301819
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.296735
obj = -21.730061, rho = -0.322014
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.251559
obj = -25.392093, rho = -0.240694
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.202010
obj = -29.023010, rho = -0.206482
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.163605
obj = -33.053569, rho = -0.146862
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.133709
obj = -36.968549, rho = -0.148029
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.104828
obj = -39.759257, rho = -0.119442
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.080610
obj = -41.876126, rho = -0.236675
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.873587, rho = 0.878403
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.239954, rho = 0.825089
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.749143, rho = 0.748399
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.444736, rho = 0.638085
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.369061, rho = 0.479404
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.540886, rho = 0.251148
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 77% (77/100) (classification)
Accuracy = 73.2% (732/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -5.900045, rho = -0.077186
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.805421
obj = -7.409678, rho = -0.104431
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.705354
obj = -9.221752, rho = -0.113882
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.615424
obj = -11.387801, rho = -0.070434
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.537293
obj = -13.867524, rho = -0.128315
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.457149
obj = -16.731320, rho = -0.098906
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.391789
obj = -20.038694, rho = -0.016099
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.324253
obj = -23.708138, rho = -0.001716
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.266391
obj = -28.028038, rho = 0.043384
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.217474
obj = -33.032139, rho = 0.056468
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.183729
obj = -38.614654, rho = 0.186022
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.146658
obj = -44.724864, rho = 0.214357
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.116526
obj = -52.430440, rho = 0.184630
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.095583
obj = -62.172781, rho = 0.245535
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.911256, rho = -0.947393
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.292667, rho = -0.924327
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.821926, rho = -0.891148
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.543135, rho = -0.843422
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.497577, rho = -0.774770
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.698796, rho = -0.676018
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 81% (81/100) (classification)
Accuracy = 71.4% (714/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.071421, rho = -0.553241
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 91.2% (912/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.837087
obj = -7.558885, rho = -0.521613
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.730302
obj = -9.314473, rho = -0.473504
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.620000
obj = -11.442089, rho = -0.469086
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.526757
obj = -14.110506, rho = -0.474252
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.461235
obj = -17.348459, rho = -0.440054
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.391221
obj = -21.227245, rho = -0.478141
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.349948
obj = -25.629887, rho = -0.339904
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.292293
obj = -30.290856, rho = -0.427469
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 247
nu = 0.234902
obj = -35.312692, rho = -0.466621
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.195561
obj = -41.751080, rho = -0.457464
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.158808
obj = -48.562013, rho = -0.428804
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 200
nu = 0.127627
obj = -56.953335, rho = -0.480510
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.103451
obj = -66.765679, rho = -0.552172
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.838459, rho = 0.913247
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 46.9% (469/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.192510, rho = 0.875409
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 46.9% (469/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -1.687263, rho = 0.820783
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 46.9% (469/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -2.368897, rho = 0.742205
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 46.9% (469/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -3.287227, rho = 0.629175
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 46.9% (469/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -4.479570, rho = 0.466825
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 62% (62/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -5.928538, rho = 0.233055
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 87% (87/100) (classification)
Accuracy = 83.5% (835/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.826268
obj = -7.535866, rho = 0.022466
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.720000
obj = -9.352920, rho = 0.002637
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.629800
obj = -11.470365, rho = -0.086760
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.550596
obj = -13.894563, rho = -0.095017
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.466744
obj = -16.554624, rho = -0.100028
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.388103
obj = -19.491852, rho = -0.037535
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.319851
obj = -22.703483, rho = -0.037258
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 187
nu = 0.255458
obj = -26.469296, rho = -0.060900
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.203793
obj = -31.246963, rho = -0.023201
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.166744
obj = -37.330831, rho = 0.091577
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.140421
obj = -44.225137, rho = 0.073986
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.116023
obj = -52.804102, rho = 0.122045
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.096427
obj = -62.548880, rho = 0.138376
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.878951, rho = -0.948632
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.251051, rho = -0.926109
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.772106, rho = -0.893712
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.492249, rho = -0.847110
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.467373, rho = -0.780075
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.744307, rho = -0.683649
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 64% (64/100) (classification)
Accuracy = 54.8% (548/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.320952, rho = -0.544944
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 86% (86/100) (classification)
Accuracy = 83.9% (839/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.855114
obj = -8.099786, rho = -0.472257
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.765594
obj = -10.213950, rho = -0.428132
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.689644
obj = -12.765242, rho = -0.312794
nSV = 70, nBSV = 68
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.601208
obj = -15.554063, rho = -0.312127
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.511508
obj = -18.786444, rho = -0.277004
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.426556
obj = -22.693601, rho = -0.305190
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.364310
obj = -27.375191, rho = -0.287379
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.308102
obj = -32.721287, rho = -0.232570
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.248497
obj = -39.152342, rho = -0.217305
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.211500
obj = -47.184720, rho = -0.276461
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.178780
obj = -56.190290, rho = -0.332849
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.150836
obj = -65.754020, rho = -0.481467
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.122958
obj = -75.310664, rho = -0.626229
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.928598, rho = 0.843119
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -1.315938, rho = 0.773600
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -1.851933, rho = 0.674335
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.579124, rho = 0.531548
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48% (480/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.534500, rho = 0.326155
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 60% (60/100) (classification)
Accuracy = 54.9% (549/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.721191, rho = 0.030707
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 86% (86/100) (classification)
Accuracy = 82.5% (825/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.914358
obj = -6.072955, rho = -0.167170
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -7.707893, rho = -0.212689
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.734371
obj = -9.705788, rho = -0.166369
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.658228
obj = -12.014480, rho = -0.236180
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.569943
obj = -14.583212, rho = -0.170487
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.480740
obj = -17.496941, rho = -0.151775
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.405512
obj = -20.835215, rho = -0.184473
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.333925
obj = -24.746862, rho = -0.255900
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.274024
obj = -29.476783, rho = -0.330461
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.227828
obj = -35.305755, rho = -0.384197
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.189808
obj = -42.172238, rho = -0.351475
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.160551
obj = -50.504818, rho = -0.275640
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.134654
obj = -58.646917, rho = -0.294689
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.107132
obj = -68.588609, rho = -0.263042
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.952996, rho = -0.924969
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.353806, rho = -0.892072
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.912143, rho = -0.844750
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.677606, rho = -0.776681
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.700729, rho = -0.678767
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 53% (53/100) (classification)
Accuracy = 53.8% (538/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.011137, rho = -0.537922
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 86% (86/100) (classification)
Accuracy = 78.9% (789/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.573143, rho = -0.430160
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 91% (91/100) (classification)
Accuracy = 91.7% (917/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.892644
obj = -8.383071, rho = -0.355334
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.792010
obj = -10.567586, rho = -0.335050
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.696332
obj = -13.252526, rho = -0.313113
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.627271
obj = -16.377358, rho = -0.285764
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.542221
obj = -19.828339, rho = -0.324247
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.464427
obj = -23.845018, rho = -0.249543
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.381441
obj = -28.184132, rho = -0.233417
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.318031
obj = -33.239419, rho = -0.341744
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.259056
obj = -39.331527, rho = -0.300459
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.213431
obj = -46.525841, rho = -0.339142
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.177747
obj = -54.652795, rho = -0.332312
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.147236
obj = -63.801755, rho = -0.315773
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.122096
obj = -71.986451, rho = -0.373148
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.916195, rho = 0.884680
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.302887, rho = 0.834118
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.843073, rho = 0.761387
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.586890, rho = 0.656767
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.588112, rho = 0.506277
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.886125, rho = 0.289804
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 70% (70/100) (classification)
Accuracy = 67.4% (674/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.459031, rho = -0.021581
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.871395
obj = -8.238051, rho = -0.076524
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 94% (94/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -10.360410, rho = -0.034009
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 95% (95/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.696669
obj = -12.861519, rho = -0.047159
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.600078
obj = -15.761300, rho = -0.093317
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.513618
obj = -19.260614, rho = -0.046570
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.433432
obj = -23.459860, rho = -0.021131
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.370822
obj = -28.491693, rho = -0.045760
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.316976
obj = -34.530616, rho = -0.125964
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.269897
obj = -41.427391, rho = -0.101567
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.220552
obj = -49.544403, rho = -0.081876
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.188712
obj = -59.173092, rho = -0.144839
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.160870
obj = -69.668145, rho = -0.224648
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.132144
obj = -79.124803, rho = -0.290836
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.873640, rho = 0.864814
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.240062, rho = 0.805542
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.749367, rho = 0.720282
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -2.445200, rho = 0.597640
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.370021, rho = 0.421225
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.542872, rho = 0.167461
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 82% (82/100) (classification)
Accuracy = 75.9% (759/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.882120
obj = -5.909163, rho = -0.114436
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.812734
obj = -7.442513, rho = -0.182487
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.722882
obj = -9.187483, rho = -0.175404
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.629718
obj = -11.218721, rho = -0.156001
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.520123
obj = -13.642547, rho = -0.166694
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.441961
obj = -16.718526, rho = -0.178321
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.371457
obj = -20.506483, rho = -0.163243
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.321184
obj = -25.219313, rho = -0.200197
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.268898
obj = -31.162654, rho = -0.262297
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.237612
obj = -38.599462, rho = -0.303440
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.208538
obj = -46.527895, rho = -0.305336
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.168238
obj = -56.392257, rho = -0.271422
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.142947
obj = -68.781075, rho = -0.162189
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.118946
obj = -84.944483, rho = -0.135601
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.929767, rho = 0.840535
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.318355, rho = 0.770618
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.856935, rho = 0.670045
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.589473, rho = 0.525376
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.555913, rho = 0.317277
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 58% (58/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.765496, rho = 0.017938
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 80% (80/100) (classification)
Accuracy = 79.8% (798/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.936042
obj = -6.149934, rho = -0.263106
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.847331
obj = -7.760038, rho = -0.283619
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.752623
obj = -9.653834, rho = -0.279518
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.664886
obj = -11.801124, rho = -0.167537
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.564045
obj = -14.236589, rho = -0.189204
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.480000
obj = -16.849283, rho = -0.150303
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.393678
obj = -19.762254, rho = -0.198687
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.317115
obj = -23.165679, rho = -0.177688
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.253311
obj = -27.645334, rho = -0.222501
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.215720
obj = -33.060827, rho = -0.265283
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.175779
obj = -39.640270, rho = -0.328452
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.143603
obj = -48.132459, rho = -0.356941
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.122686
obj = -58.831505, rho = -0.347456
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.109986
obj = -69.726816, rho = -0.225507
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.927956, rho = -0.917878
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.314609, rho = -0.881872
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.849183, rho = -0.830078
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.573433, rho = -0.755576
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.522724, rho = -0.648409
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 61% (61/100) (classification)
Accuracy = 60.2% (602/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.696824, rho = -0.494253
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 86% (86/100) (classification)
Accuracy = 83.3% (833/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.912526
obj = -6.054171, rho = -0.366158
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 91.7% (917/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.822973
obj = -7.645675, rho = -0.318751
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.733460
obj = -9.543580, rho = -0.284601
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.642602
obj = -11.810679, rho = -0.257958
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.554456
obj = -14.415405, rho = -0.193757
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.467920
obj = -17.551992, rho = -0.170287
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.394673
obj = -21.486842, rho = -0.166937
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.335234
obj = -26.292658, rho = -0.141594
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.295304
obj = -32.139694, rho = -0.083846
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.250978
obj = -38.483187, rho = -0.074311
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.207192
obj = -45.732273, rho = -0.120712
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.176101
obj = -53.913773, rho = -0.031223
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.140798
obj = -63.074449, rho = -0.020461
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.124633
obj = -72.277633, rho = -0.186804
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -0.744512, rho = 0.933682
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -1.061174, rho = 0.904605
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -1.506234, rho = 0.862779
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -2.124820, rho = 0.802615
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -2.969913, rho = 0.716071
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -4.093019, rho = 0.591583
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -5.517119, rho = 0.412512
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 73% (73/100) (classification)
Accuracy = 62.2% (622/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -7.169519, rho = 0.154928
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 93% (93/100) (classification)
Accuracy = 87.9% (879/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.700000
obj = -8.908775, rho = 0.050846
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.602940
obj = -10.843516, rho = -0.011029
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.511496
obj = -13.133903, rho = -0.022084
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.442871
obj = -15.649351, rho = -0.091912
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.360253
obj = -18.519172, rho = -0.093951
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.291875
obj = -22.185024, rho = -0.131407
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.239624
obj = -26.887438, rho = -0.052532
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.212117
obj = -32.600623, rho = -0.088491
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.179080
obj = -38.724630, rho = -0.060509
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.144437
obj = -45.584821, rho = -0.083398
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.120058
obj = -53.652313, rho = -0.261700
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.099891
obj = -62.786570, rho = -0.203039
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.931510, rho = -0.916579
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.321961, rho = -0.880003
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.864396, rho = -0.827390
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.604911, rho = -0.751710
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.587856, rho = -0.642847
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.831591, rho = -0.486253
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 78% (78/100) (classification)
Accuracy = 75.8% (758/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.952748
obj = -6.269121, rho = -0.287353
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.847967
obj = -7.909378, rho = -0.213797
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -9.930192, rho = -0.189257
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.671194
obj = -12.286208, rho = -0.137229
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.578699
obj = -15.007402, rho = -0.125600
nSV = 61, nBSV = 53
Total nSV = 61
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.491222
obj = -18.270358, rho = -0.068553
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.413902
obj = -22.179163, rho = -0.049316
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.353312
obj = -26.910430, rho = -0.035069
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.295700
obj = -32.447465, rho = -0.020865
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.248207
obj = -39.271601, rho = 0.032479
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.208142
obj = -47.387824, rho = 0.028842
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.173318
obj = -57.631724, rho = -0.031711
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.151277
obj = -69.739563, rho = -0.134862
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*..*
optimization finished, #iter = 387
nu = 0.124670
obj = -83.130054, rho = -0.158921
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.951028, rho = -0.882040
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.349734, rho = -0.830320
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.903717, rho = -0.755924
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.660171, rho = -0.648910
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.664655, rho = -0.494974
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 59% (59/100) (classification)
Accuracy = 56.7% (567/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.936496, rho = -0.273545
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 88% (88/100) (classification)
Accuracy = 89.5% (895/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.965742
obj = -6.410870, rho = -0.164594
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 92% (92/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.879247
obj = -8.101915, rho = -0.145418
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 91% (91/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.779305
obj = -10.097734, rho = -0.102505
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 95% (95/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.677650
obj = -12.440062, rho = -0.091876
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 95% (95/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.579263
obj = -15.233166, rho = -0.055648
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 95% (95/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.481240
obj = -18.772830, rho = -0.029048
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 95% (95/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.419541
obj = -23.272209, rho = -0.052510
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 95% (95/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.357961
obj = -28.962700, rho = -0.058338
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 95% (95/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.312087
obj = -36.208944, rho = -0.085107
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 96% (96/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 22
nu = 0.267795
obj = -45.080711, rho = -0.091734
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 99.5% (995/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.232882
obj = -56.407575, rho = -0.142222
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.197183
obj = -70.863330, rho = -0.173601
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.175461
obj = -89.883549, rho = -0.277110
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.152457
obj = -113.950314, rho = -0.268828
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.954743, rho = -0.898952
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.357420, rho = -0.854648
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.919621, rho = -0.790918
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.693079, rho = -0.699246
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.732745, rho = -0.567381
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.077383, rho = -0.377699
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 70% (70/100) (classification)
Accuracy = 67.7% (677/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.699407, rho = -0.104851
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 93% (93/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.913401
obj = -8.517237, rho = -0.048503
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.810216
obj = -10.633405, rho = -0.059978
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.715119
obj = -13.120129, rho = -0.009916
nSV = 74, nBSV = 68
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.620000
obj = -16.068173, rho = 0.111718
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.517834
obj = -19.587339, rho = 0.094421
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.443404
obj = -23.961795, rho = 0.090418
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.368857
obj = -29.530413, rho = 0.107237
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.310909
obj = -36.872945, rho = 0.078465
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.278457
obj = -46.052513, rho = 0.072237
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.241515
obj = -57.032843, rho = -0.005711
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.202590
obj = -70.836571, rho = -0.061309
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.177037
obj = -88.951638, rho = -0.051334
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.150490
obj = -111.839053, rho = -0.044557
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.898827, rho = -0.938736
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.279565, rho = -0.911875
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.812960, rho = -0.873237
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.550682, rho = -0.817657
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.550735, rho = -0.737709
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.862790, rho = -0.622708
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 60% (60/100) (classification)
Accuracy = 60% (600/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.488429, rho = -0.457284
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 86% (86/100) (classification)
Accuracy = 87% (870/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.341899, rho = -0.320494
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 92% (92/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.799775
obj = -10.465995, rho = -0.270263
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 93% (93/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.682693
obj = -12.995649, rho = -0.281057
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 94% (94/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.596734
obj = -16.198289, rho = -0.318933
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.522767
obj = -20.123503, rho = -0.172246
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.451046
obj = -24.746530, rho = -0.107733
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.381102
obj = -30.507035, rho = -0.184534
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.328090
obj = -37.779379, rho = -0.182513
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.290474
obj = -46.505896, rho = -0.212622
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.249161
obj = -56.053909, rho = -0.217708
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.214531
obj = -66.520029, rho = -0.169875
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.175292
obj = -77.755858, rho = -0.068680
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.139313
obj = -92.241259, rho = -0.059051
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.966197, rho = -0.020834
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 89.1% (891/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.368507, rho = -0.029969
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 89.1% (891/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.924416, rho = -0.043109
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 89.1% (891/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.676901, rho = -0.062010
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 89.1% (891/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.661729, rho = -0.089199
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 89.1% (891/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.996723
obj = -4.876542, rho = -0.126587
nSV = 100, nBSV = 98
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.950122
obj = -6.304943, rho = -0.152017
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.977604, rho = -0.199985
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.774063
obj = -9.898147, rho = -0.147674
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.672942
obj = -12.101608, rho = -0.190164
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.573435
obj = -14.681401, rho = -0.144745
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.480000
obj = -17.739442, rho = -0.141319
nSV = 50, nBSV = 47
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.411007
obj = -21.267177, rho = -0.164198
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.341696
obj = -25.157306, rho = -0.210075
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.281876
obj = -29.677603, rho = -0.251327
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.229701
obj = -35.356740, rho = -0.262739
nSV = 25, nBSV = 22
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.189386
obj = -42.168297, rho = -0.263636
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.157034
obj = -50.168801, rho = -0.260258
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.130840
obj = -59.856636, rho = -0.177014
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.107467
obj = -71.312804, rho = -0.225828
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -0.821969, rho = -0.953085
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -1.170988, rho = -0.932515
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -1.660877, rho = -0.902927
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -2.340401, rho = -0.860365
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -3.265806, rho = -0.799142
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 58% (58/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -4.489248, rho = -0.711075
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 59% (59/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -6.026246, rho = -0.584396
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 75% (75/100) (classification)
Accuracy = 70.6% (706/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.827731
obj = -7.780748, rho = -0.426556
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 90.5% (905/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.757165
obj = -9.654544, rho = -0.344860
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.659695
obj = -11.771587, rho = -0.273104
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.561235
obj = -14.173807, rho = -0.198106
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.483672
obj = -16.810373, rho = -0.237522
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.395726
obj = -19.604175, rho = -0.271078
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.321670
obj = -22.804654, rho = -0.348813
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.255749
obj = -26.531582, rho = -0.330760
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.209695
obj = -30.668992, rho = -0.348506
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.169658
obj = -35.244215, rho = -0.384689
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.134792
obj = -40.843320, rho = -0.447641
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 340
nu = 0.111482
obj = -46.835750, rho = -0.454293
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.089831
obj = -53.064816, rho = -0.511850
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.913822, rho = -0.923540
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.297977, rho = -0.890016
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.832912, rho = -0.841794
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.565866, rho = -0.772428
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.544611, rho = -0.672650
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.796115, rho = -0.529123
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 76% (76/100) (classification)
Accuracy = 70.4% (704/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.936673
obj = -6.272930, rho = -0.327869
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 94% (94/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860589
obj = -7.915955, rho = -0.203031
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.766966
obj = -9.755839, rho = -0.118027
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.665228
obj = -11.919039, rho = -0.145962
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.562547
obj = -14.356153, rho = -0.118757
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.480000
obj = -17.206651, rho = -0.222033
nSV = 49, nBSV = 47
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.397495
obj = -20.284814, rho = -0.195425
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.321576
obj = -24.132416, rho = -0.220476
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.272057
obj = -28.822658, rho = -0.285636
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.228649
obj = -34.038106, rho = -0.254117
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.185031
obj = -40.170666, rho = -0.302257
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.161179
obj = -45.712577, rho = -0.173188
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 174
nu = 0.123256
obj = -51.081238, rho = -0.187266
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.096263
obj = -57.973090, rho = -0.247589
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.970870, rho = -0.033102
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.378177, rho = -0.047616
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.944425, rho = -0.068493
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.718302, rho = -0.098523
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.747393, rho = -0.141721
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.053688, rho = -0.203858
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 92% (92/100) (classification)
Accuracy = 90.9% (909/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.955928
obj = -6.624819, rho = -0.274777
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 91% (91/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.596477, rho = -0.275883
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.805058
obj = -11.010181, rho = -0.238731
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.739809
obj = -13.793721, rho = -0.310736
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.639076
obj = -17.000881, rho = -0.326853
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.558606
obj = -20.811427, rho = -0.214431
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.476377
obj = -25.270209, rho = -0.203901
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.399727
obj = -30.603976, rho = -0.286574
nSV = 44, nBSV = 35
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.339380
obj = -37.118151, rho = -0.326771
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.281180
obj = -44.955175, rho = -0.356486
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.238584
obj = -54.680657, rho = -0.356172
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.204198
obj = -66.602966, rho = -0.342398
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.168573
obj = -80.844628, rho = -0.385268
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.142626
obj = -98.393744, rho = -0.298640
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.914637, rho = 0.880436
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.299665, rho = 0.827485
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.836405, rho = 0.751846
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.573093, rho = 0.643043
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.559564, rho = 0.486535
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.827055, rho = 0.261406
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 70% (70/100) (classification)
Accuracy = 67.2% (672/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.349706, rho = 0.012373
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 91% (91/100) (classification)
Accuracy = 88% (880/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -8.076448, rho = -0.186750
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.780000
obj = -9.983396, rho = -0.246413
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.671312
obj = -12.233353, rho = -0.191138
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.578054
obj = -14.896014, rho = -0.151661
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.492237
obj = -17.980810, rho = -0.098430
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.412662
obj = -21.534765, rho = -0.121667
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.342586
obj = -25.796316, rho = -0.124378
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.285013
obj = -30.850171, rho = -0.180416
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.239390
obj = -37.024377, rho = -0.146783
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*.*
optimization finished, #iter = 332
nu = 0.195034
obj = -44.427280, rho = -0.136542
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.158546
obj = -54.369696, rho = -0.090541
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.133844
obj = -68.057034, rho = -0.092640
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.116832
obj = -85.812735, rho = -0.102701
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.842365, rho = 0.916504
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.200578, rho = 0.879896
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.703959, rho = 0.827236
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.403443, rho = 0.751488
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.358707, rho = 0.642528
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.627468, rho = 0.485794
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -6.234560, rho = 0.260340
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 78% (78/100) (classification)
Accuracy = 76.7% (767/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.095296, rho = -0.063964
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.794530
obj = -10.139625, rho = -0.058604
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.685820
obj = -12.415222, rho = -0.065604
nSV = 72, nBSV = 65
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.591975
obj = -15.031299, rho = -0.056750
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.500000
obj = -17.998055, rho = -0.006384
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.420902
obj = -21.286071, rho = -0.029384
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.344755
obj = -24.804229, rho = -0.108648
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.286832
obj = -28.815028, rho = -0.168626
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.232464
obj = -32.645476, rho = -0.205337
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.........*
optimization finished, #iter = 933
nu = 0.180556
obj = -36.897366, rho = -0.223469
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.143057
obj = -42.161609, rho = -0.278848
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.109970
obj = -48.742899, rho = -0.276529
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.089085
obj = -57.211553, rho = -0.257002
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -0.839378, rho = 0.910605
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.194396, rho = 0.871410
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -1.691168, rho = 0.814916
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -2.376977, rho = 0.733765
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -3.303949, rho = 0.617008
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 57% (57/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -4.514166, rho = 0.449086
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 64% (64/100) (classification)
Accuracy = 58% (580/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.000125, rho = 0.207622
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 88% (88/100) (classification)
Accuracy = 83.1% (831/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.828028
obj = -7.643307, rho = 0.068684
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.733159
obj = -9.509854, rho = 0.062470
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.630495
obj = -11.754673, rho = 0.134264
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.552912
obj = -14.520145, rho = 0.085328
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.464928
obj = -17.835233, rho = 0.064742
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.401255
obj = -21.922431, rho = 0.005791
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.341500
obj = -26.929220, rho = 0.043184
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.283254
obj = -33.491746, rho = 0.049544
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.253947
obj = -41.417097, rho = -0.129928
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.216289
obj = -51.336005, rho = -0.116373
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.189905
obj = -63.345272, rho = -0.197203
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.158770
obj = -77.609917, rho = -0.188862
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.135105
obj = -95.964326, rho = -0.275845
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -0.784427, rho = -0.963795
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -1.118537, rho = -0.947922
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -1.588638, rho = -0.925088
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -2.243126, rho = -0.892242
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -3.139618, rho = -0.844996
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 60% (60/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -4.336154, rho = -0.777035
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 61% (61/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -5.864835, rho = -0.679276
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 78% (78/100) (classification)
Accuracy = 68.7% (687/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.786238
obj = -7.670953, rho = -0.554012
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 88% (88/100) (classification)
Accuracy = 87.3% (873/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.726102
obj = -9.793422, rho = -0.513673
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 93% (93/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.646901
obj = -12.306982, rho = -0.479657
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.566558
obj = -15.328280, rho = -0.429017
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.496721
obj = -18.815867, rho = -0.294092
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.419729
obj = -23.155421, rho = -0.269423
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.359805
obj = -28.542434, rho = -0.253067
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.309139
obj = -35.166682, rho = -0.309626
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.260563
obj = -43.611404, rho = -0.343526
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.228800
obj = -53.850305, rho = -0.333309
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.197811
obj = -66.117856, rho = -0.343231
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.170262
obj = -80.571640, rho = -0.347791
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 193
nu = 0.140865
obj = -97.511492, rho = -0.384569
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.915424, rho = 0.875390
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.301291, rho = 0.820755
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.839771, rho = 0.742165
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.580058, rho = 0.629117
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.573975, rho = 0.466504
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.856874, rho = 0.232592
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 79% (79/100) (classification)
Accuracy = 77.5% (775/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.398506, rho = -0.103878
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.133144, rho = -0.110429
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.803742
obj = -10.083827, rho = -0.056047
nSV = 83, nBSV = 77
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.695183
obj = -12.220747, rho = -0.032385
nSV = 73, nBSV = 67
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.586132
obj = -14.606508, rho = -0.040404
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.485718
obj = -17.429989, rho = -0.022480
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.402095
obj = -20.763314, rho = 0.069123
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.336870
obj = -24.618617, rho = 0.046047
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.284644
obj = -28.796579, rho = 0.098085
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.227122
obj = -33.249412, rho = 0.132599
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.183516
obj = -38.564827, rho = 0.145903
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.147643
obj = -44.359472, rho = 0.073808
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.119987
obj = -50.837785, rho = 0.051101
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*...*
optimization finished, #iter = 466
nu = 0.097412
obj = -57.745171, rho = -0.020885
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.895865, rho = -0.931405
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.273435, rho = -0.901329
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.800277, rho = -0.858067
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.524439, rho = -0.795837
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.496435, rho = -0.706321
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.750435, rho = -0.577558
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 68% (68/100) (classification)
Accuracy = 58.9% (589/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.255951, rho = -0.392338
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 92% (92/100) (classification)
Accuracy = 91.3% (913/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.908621, rho = -0.294055
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.773558
obj = -9.722594, rho = -0.262057
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.662797
obj = -11.731167, rho = -0.249618
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.554624
obj = -14.194304, rho = -0.229456
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.470541
obj = -17.003631, rho = -0.238406
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.394276
obj = -20.307338, rho = -0.130824
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.332378
obj = -23.891802, rho = -0.056207
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.268591
obj = -27.894868, rho = -0.095367
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.221838
obj = -32.380321, rho = -0.140354
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.177437
obj = -37.668954, rho = -0.154973
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.143152
obj = -43.882491, rho = -0.153330
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.118647
obj = -50.901568, rho = -0.128486
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.096777
obj = -57.839931, rho = -0.091799
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -0.804645, rho = 0.923130
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -1.147756, rho = 0.889426
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -1.630952, rho = 0.840944
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -2.304580, rho = 0.771207
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -3.229234, rho = 0.670930
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -4.467579, rho = 0.526650
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 59% (59/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -6.059093, rho = 0.318123
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 72% (72/100) (classification)
Accuracy = 67.2% (672/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -7.955713, rho = 0.018744
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 90% (90/100) (classification)
Accuracy = 91% (910/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.760000
obj = -10.081839, rho = -0.084546
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.681341
obj = -12.510182, rho = -0.142851
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.582163
obj = -15.332966, rho = -0.197110
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.499989
obj = -18.753946, rho = -0.215023
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.422832
obj = -22.857082, rho = -0.169228
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.352257
obj = -27.932659, rho = -0.171498
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.308356
obj = -34.292802, rho = -0.210543
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.260591
obj = -41.638284, rho = -0.252435
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.224483
obj = -50.087212, rho = -0.294745
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.192167
obj = -59.577742, rho = -0.395222
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.161438
obj = -69.420358, rho = -0.302544
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.137254
obj = -79.353383, rho = -0.554932
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.892060, rho = -0.938784
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.265561, rho = -0.911944
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.783985, rho = -0.873336
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.490728, rho = -0.817800
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.426682, rho = -0.737915
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 55% (55/100) (classification)
Accuracy = 53% (530/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.606108, rho = -0.623003
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 82% (82/100) (classification)
Accuracy = 74.3% (743/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -5.984583, rho = -0.495330
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 88.7% (887/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.542984, rho = -0.442500
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.725184
obj = -9.379362, rho = -0.400847
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.621319
obj = -11.583405, rho = -0.381528
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.557383
obj = -14.208687, rho = -0.269693
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.468940
obj = -17.098253, rho = -0.282903
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.396057
obj = -20.388209, rho = -0.317171
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.328753
obj = -24.234128, rho = -0.357270
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.272064
obj = -28.598966, rho = -0.327582
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.228527
obj = -33.184747, rho = -0.354585
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.187212
obj = -37.970496, rho = -0.359788
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.152665
obj = -42.453945, rho = -0.375390
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 273
nu = 0.117676
obj = -46.437812, rho = -0.404385
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.088443
obj = -50.412172, rho = -0.418841
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.950969, rho = 0.846553
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.349611, rho = 0.779274
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.903462, rho = 0.682496
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.659644, rho = 0.543287
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.663565, rho = 0.343041
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 55% (55/100) (classification)
Accuracy = 53.5% (535/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.934240, rho = 0.054998
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 83% (83/100) (classification)
Accuracy = 83.9% (839/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.954630
obj = -6.428811, rho = -0.231626
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 95% (95/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.871738
obj = -8.202295, rho = -0.363107
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.789118
obj = -10.258188, rho = -0.320949
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.698122
obj = -12.605182, rho = -0.221051
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.588589
obj = -15.255586, rho = -0.190955
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.504368
obj = -18.379332, rho = -0.164896
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.425070
obj = -22.058229, rho = -0.137707
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.352059
obj = -26.355144, rho = -0.136624
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.291315
obj = -31.707872, rho = -0.113625
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.243245
obj = -38.073760, rho = -0.221147
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.206398
obj = -45.568757, rho = -0.273404
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.167372
obj = -54.485776, rho = -0.286256
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.142389
obj = -65.436902, rho = -0.306179
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.122097
obj = -78.227418, rho = -0.407574
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.910866, rho = 0.857002
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.291860, rho = 0.794305
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -1.820257, rho = 0.704118
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -2.539680, rho = 0.574388
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.490429, rho = 0.387778
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 57% (57/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.684004, rho = 0.119350
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 86% (86/100) (classification)
Accuracy = 77.5% (775/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.049414, rho = -0.128200
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.835355
obj = -7.577141, rho = -0.124915
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.736715
obj = -9.313903, rho = -0.099944
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.639495
obj = -11.293796, rho = -0.089735
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.536031
obj = -13.556401, rho = -0.084715
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.454657
obj = -16.186904, rho = -0.051822
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.372685
obj = -19.222278, rho = -0.061044
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.307926
obj = -22.809091, rho = -0.034507
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.253235
obj = -27.266640, rho = -0.025523
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.209790
obj = -32.611729, rho = -0.015035
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.173296
obj = -39.258163, rho = 0.039362
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.148907
obj = -47.014331, rho = 0.236544
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.120280
obj = -56.291903, rho = 0.265906
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.101393
obj = -68.171364, rho = 0.343203
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.877292, rho = 0.881214
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.247620, rho = 0.829132
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.765005, rho = 0.754215
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.477557, rho = 0.646450
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.436972, rho = 0.491436
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.681403, rho = 0.268456
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 74% (74/100) (classification)
Accuracy = 65.3% (653/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.888720
obj = -6.192664, rho = -0.000495
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 91% (91/100) (classification)
Accuracy = 86.4% (864/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -7.932984, rho = -0.145935
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 93% (93/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.748602
obj = -9.995424, rho = -0.176942
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.662578
obj = -12.438575, rho = -0.158291
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.570381
obj = -15.416059, rho = -0.135845
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.494524
obj = -19.087095, rho = -0.132205
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.432973
obj = -23.535103, rho = -0.137943
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.380261
obj = -28.444781, rho = -0.127939
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.311829
obj = -34.089800, rho = -0.152794
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.258604
obj = -41.374364, rho = -0.170868
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.216186
obj = -50.273153, rho = -0.160519
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.185190
obj = -61.724082, rho = -0.127030
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.163191
obj = -74.431238, rho = -0.190588
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.139563
obj = -86.928028, rho = -0.344534
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.898427, rho = 0.884083
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.278736, rho = 0.833259
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.811245, rho = 0.760151
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.547134, rho = 0.654989
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.543393, rho = 0.503719
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.847599, rho = 0.286124
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 71% (71/100) (classification)
Accuracy = 62.6% (626/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.456997, rho = -0.026874
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 90% (900/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.336697, rho = -0.094628
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.788123
obj = -10.558074, rho = -0.103804
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.696471
obj = -13.218493, rho = -0.004970
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.624486
obj = -16.298709, rho = 0.030633
nSV = 64, nBSV = 62
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.525365
obj = -19.806608, rho = 0.038556
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.460483
obj = -23.798749, rho = 0.065916
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.388654
obj = -28.240531, rho = 0.115024
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.317365
obj = -33.052238, rho = 0.048929
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.261056
obj = -38.466187, rho = -0.017691
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.215685
obj = -44.500562, rho = -0.043205
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.174681
obj = -50.597821, rho = -0.038217
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.137850
obj = -56.386291, rho = -0.002139
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*...*
optimization finished, #iter = 392
nu = 0.107723
obj = -62.143180, rho = 0.036243
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.951354, rho = 0.839353
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.350409, rho = 0.768917
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.905114, rho = 0.667599
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.663062, rho = 0.521858
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.670636, rho = 0.312216
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 56% (56/100) (classification)
Accuracy = 56.7% (567/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.948871, rho = 0.010658
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 84.8% (848/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.962023
obj = -6.436991, rho = -0.285510
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 93% (93/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.871664
obj = -8.195625, rho = -0.301849
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.789877
obj = -10.256841, rho = -0.242308
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.703488
obj = -12.640796, rho = -0.203333
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.592113
obj = -15.278023, rho = -0.164078
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.502690
obj = -18.509084, rho = -0.144736
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.420000
obj = -22.479508, rho = -0.137345
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.356621
obj = -27.146229, rho = -0.107252
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.298615
obj = -32.708720, rho = -0.061309
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.246353
obj = -39.731523, rho = -0.063159
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.206313
obj = -48.948337, rho = -0.046933
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.184466
obj = -60.132034, rho = -0.063756
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.152905
obj = -72.315271, rho = -0.093216
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.124699
obj = -88.960142, rho = -0.124562
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.945329, rho = 0.833660
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.337942, rho = 0.760728
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.879317, rho = 0.655820
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.609684, rho = 0.504914
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.560191, rho = 0.287843
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 67% (67/100) (classification)
Accuracy = 59.7% (597/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.720344, rho = -0.024402
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 93% (93/100) (classification)
Accuracy = 90.1% (901/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.927100
obj = -6.015391, rho = -0.209242
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.819818
obj = -7.551033, rho = -0.177153
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.736248
obj = -9.374116, rho = -0.167804
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.635271
obj = -11.424415, rho = -0.105911
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.540780
obj = -13.807703, rho = -0.149919
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.457247
obj = -16.589941, rho = -0.174757
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.381785
obj = -19.874227, rho = -0.103679
nSV = 40, nBSV = 37
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.318253
obj = -23.653151, rho = -0.217580
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.264689
obj = -28.171835, rho = -0.202180
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.222472
obj = -32.829392, rho = -0.170340
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.179831
obj = -38.495891, rho = -0.231003
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.145031
obj = -44.867024, rho = -0.312845
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.117833
obj = -52.829119, rho = -0.359539
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.097367
obj = -61.865981, rho = -0.468241
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.932180, rho = 0.885570
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.323349, rho = 0.835399
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.867267, rho = 0.763229
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.610851, rho = 0.659417
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.600148, rho = 0.510088
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 48.3% (483/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.857025, rho = 0.295286
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 77% (77/100) (classification)
Accuracy = 75% (750/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.321137, rho = -0.013695
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.863424
obj = -7.929710, rho = -0.083351
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.762553
obj = -9.831652, rho = -0.050564
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.672765
obj = -12.062436, rho = 0.022260
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.576401
obj = -14.483849, rho = -0.065538
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.487361
obj = -17.253044, rho = -0.013784
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.406828
obj = -20.276930, rho = 0.025764
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.328117
obj = -23.758567, rho = 0.012402
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.269954
obj = -27.828187, rho = -0.032302
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.222801
obj = -32.245013, rho = -0.039911
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.176965
obj = -37.009038, rho = -0.018581
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.140006
obj = -42.872437, rho = 0.003226
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.110798
obj = -50.538283, rho = -0.056343
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.090847
obj = -60.465359, rho = -0.032729
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.964260, rho = -0.042750
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.364498, rho = -0.061494
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.916121, rho = -0.088456
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.659739, rho = -0.127240
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.626217, rho = -0.183028
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.811379, rho = -0.231959
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 96% (96/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.934423
obj = -6.183604, rho = -0.137057
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.847249
obj = -7.795323, rho = -0.109749
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.757125
obj = -9.629158, rho = -0.112421
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.658377
obj = -11.720623, rho = -0.097871
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.555890
obj = -14.121655, rho = -0.107360
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.470985
obj = -16.886577, rho = -0.091489
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.389196
obj = -19.925324, rho = -0.023365
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.321538
obj = -23.620179, rho = -0.055819
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.263123
obj = -27.943349, rho = -0.103806
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.209871
obj = -33.389642, rho = -0.078642
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.175109
obj = -40.766548, rho = -0.135851
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 318
nu = 0.147453
obj = -49.892215, rho = -0.132105
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.124448
obj = -61.934103, rho = -0.114861
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.107739
obj = -77.063262, rho = -0.109630
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949295, rho = 0.847058
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.346148, rho = 0.780001
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.896296, rho = 0.683543
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.644817, rho = 0.544792
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.632885, rho = 0.345206
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 56% (56/100) (classification)
Accuracy = 57.2% (572/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.870759, rho = 0.058112
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 94% (94/100) (classification)
Accuracy = 88.1% (881/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.955920
obj = -6.297713, rho = -0.185923
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.876616
obj = -7.909084, rho = -0.165371
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.768153
obj = -9.766792, rho = -0.170916
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.672738
obj = -11.776182, rho = -0.075535
nSV = 70, nBSV = 62
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.557227
obj = -14.048909, rho = -0.069070
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.464839
obj = -16.784361, rho = -0.026388
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.383820
obj = -19.981623, rho = -0.001299
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.316583
obj = -24.007562, rho = 0.030204
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.270302
obj = -28.545469, rho = 0.113485
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.221443
obj = -33.939076, rho = 0.040109
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.181437
obj = -40.361946, rho = -0.011175
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.147425
obj = -48.770994, rho = -0.009946
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.127543
obj = -58.217971, rho = -0.152789
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.104798
obj = -69.307163, rho = -0.312142
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.914119, rho = 0.876058
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.298591, rho = 0.821716
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.834183, rho = 0.743548
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.568496, rho = 0.631106
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.550051, rho = 0.469365
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.807372, rho = 0.236708
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 78% (78/100) (classification)
Accuracy = 73.8% (738/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.928966
obj = -6.297726, rho = -0.065124
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.857946
obj = -8.012788, rho = -0.064989
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 94% (94/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.755410
obj = -10.092947, rho = -0.068035
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.677086
obj = -12.559595, rho = -0.083062
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.585522
obj = -15.398468, rho = -0.040812
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.501522
obj = -18.878321, rho = 0.010452
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.419954
obj = -23.223813, rho = 0.041981
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.357012
obj = -28.847404, rho = 0.096518
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.306584
obj = -35.944959, rho = 0.004222
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.266770
obj = -44.727611, rho = -0.053598
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.236000
obj = -55.724775, rho = -0.048406
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 299
nu = 0.206049
obj = -68.349268, rho = -0.216014
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.172614
obj = -83.756619, rho = -0.309435
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.146926
obj = -102.428509, rho = -0.478439
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.965378, rho = -0.004870
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.366813, rho = -0.007005
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.920911, rho = -0.010076
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.669649, rho = -0.014494
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.646723, rho = -0.020849
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.845387, rho = -0.029990
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.953749
obj = -6.194977, rho = -0.033066
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.856950
obj = -7.733673, rho = 0.008979
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.757625
obj = -9.534778, rho = 0.051675
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.650682
obj = -11.563455, rho = -0.030364
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.548907
obj = -13.887795, rho = 0.050508
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.460000
obj = -16.700232, rho = -0.017160
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.388694
obj = -19.883955, rho = 0.028960
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.324911
obj = -23.384207, rho = 0.081064
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.262231
obj = -27.541249, rho = 0.063564
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.215596
obj = -32.256964, rho = -0.000871
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.181383
obj = -37.176843, rho = -0.105719
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.141697
obj = -42.499204, rho = -0.084965
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.112521
obj = -49.097406, rho = -0.088967
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.091455
obj = -56.781522, rho = -0.123436
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.915764, rho = 0.887893
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 53.6% (536/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.301995, rho = 0.838740
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 53.6% (536/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.841226, rho = 0.768035
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 53.6% (536/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.583069, rho = 0.666330
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 53.6% (536/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.580206, rho = 0.520033
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 53.6% (536/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.869765, rho = 0.309591
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 66% (66/100) (classification)
Accuracy = 67.2% (672/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.425179, rho = 0.006881
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.877652
obj = -8.177909, rho = -0.129030
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.787123
obj = -10.229979, rho = -0.060329
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.690836
obj = -12.548828, rho = -0.134423
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.580000
obj = -15.431899, rho = -0.139511
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.496488
obj = -18.982636, rho = -0.168849
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.420387
obj = -23.438144, rho = -0.120212
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.374417
obj = -28.732815, rho = -0.056149
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.324190
obj = -34.488560, rho = -0.010873
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.264133
obj = -41.045091, rho = -0.022260
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.222728
obj = -48.996042, rho = 0.017845
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 114
nu = 0.184662
obj = -58.218406, rho = 0.045181
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.149391
obj = -69.382044, rho = 0.013236
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 152
nu = 0.125655
obj = -82.843030, rho = -0.004014
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.967320, rho = -0.035431
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.370831, rho = -0.050965
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.929226, rho = -0.073311
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.686854, rho = -0.105454
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.682322, rho = -0.151691
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.919048, rho = -0.218199
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940162
obj = -6.371843, rho = -0.193149
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.857707
obj = -8.141011, rho = -0.220363
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.766813
obj = -10.281185, rho = -0.111518
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.685081
obj = -12.837785, rho = -0.057364
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.597568
obj = -15.903267, rho = 0.015221
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.521083
obj = -19.506995, rho = 0.059585
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.443705
obj = -23.611101, rho = 0.006828
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.370451
obj = -28.555301, rho = 0.101786
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.314060
obj = -34.659584, rho = 0.116578
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.271584
obj = -41.701655, rho = -0.002044
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.217612
obj = -50.200436, rho = -0.018832
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 191
nu = 0.183118
obj = -61.376611, rho = -0.145071
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.153226
obj = -75.573303, rho = -0.202212
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.128458
obj = -94.552903, rho = -0.165059
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -0.787584, rho = 0.929591
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -1.125070, rho = 0.898720
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -1.602155, rho = 0.854314
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -2.271094, rho = 0.790438
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -3.197488, rho = 0.698555
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -4.455896, rho = 0.566387
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -6.112598, rho = 0.376269
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 65% (65/100) (classification)
Accuracy = 56.2% (562/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -8.178164, rho = 0.102794
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 83% (83/100) (classification)
Accuracy = 82.8% (828/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -10.543381, rho = -0.154554
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 94% (94/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.692503
obj = -13.231162, rho = -0.135540
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.606789
obj = -16.521018, rho = -0.071070
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.524907
obj = -20.624915, rho = -0.022241
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.451502
obj = -25.856054, rho = -0.020449
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.400000
obj = -32.295374, rho = -0.138410
nSV = 41, nBSV = 38
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.344384
obj = -40.178426, rho = -0.210516
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.300043
obj = -50.151724, rho = -0.255595
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.258237
obj = -62.272887, rho = -0.328909
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.230415
obj = -77.289355, rho = -0.522346
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.190005
obj = -95.552210, rho = -0.524361
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.162928
obj = -120.337866, rho = -0.526961
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -0.806551, rho = 0.935714
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.151700, rho = 0.907528
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -1.639116, rho = 0.866687
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -2.321473, rho = 0.808236
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -3.264186, rho = 0.724158
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -4.539898, rho = 0.603215
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -6.208729, rho = 0.429244
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 66% (66/100) (classification)
Accuracy = 60% (600/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -8.265331, rho = 0.178996
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 83% (83/100) (classification)
Accuracy = 88.4% (884/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -10.624831, rho = -0.005253
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 95% (95/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.690841
obj = -13.410165, rho = -0.032670
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.620000
obj = -16.745383, rho = -0.003643
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.544862
obj = -20.710847, rho = 0.006254
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.481113
obj = -25.200035, rho = 0.101633
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.399101
obj = -30.457224, rho = 0.092180
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.331027
obj = -36.956550, rho = 0.015183
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.280949
obj = -44.628818, rho = 0.115485
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.237705
obj = -54.315547, rho = 0.093235
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.202031
obj = -65.996802, rho = 0.174557
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.174617
obj = -78.905719, rho = 0.204131
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.147269
obj = -92.472058, rho = 0.184201
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.877985, rho = 0.908974
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.249052, rho = 0.869064
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.767969, rho = 0.811655
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.483688, rho = 0.729076
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -3.449663, rho = 0.611253
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -4.707662, rho = 0.440807
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 59% (59/100) (classification)
Accuracy = 55.5% (555/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -6.245128, rho = 0.195628
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 88% (88/100) (classification)
Accuracy = 82.9% (829/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.874889
obj = -7.911944, rho = -0.056328
nSV = 89, nBSV = 84
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.761165
obj = -9.788099, rho = -0.072225
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.669213
obj = -11.917471, rho = -0.168460
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.577148
obj = -14.243623, rho = -0.187138
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.482903
obj = -16.831275, rho = -0.106058
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.393759
obj = -19.630976, rho = -0.138219
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.321589
obj = -22.990214, rho = -0.119239
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.255358
obj = -26.941227, rho = -0.101372
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.214848
obj = -31.992569, rho = -0.076730
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.174931
obj = -37.640075, rho = -0.078911
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.142300
obj = -44.120100, rho = -0.134798
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.115086
obj = -51.559226, rho = 0.037697
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.091898
obj = -61.440002, rho = 0.041592
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.968195, rho = -0.042231
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.372642, rho = -0.060746
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.932972, rho = -0.087381
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.694606, rho = -0.125693
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.698362, rho = -0.180803
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.952235, rho = -0.260076
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.964618
obj = -6.415962, rho = -0.231597
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.892385
obj = -8.071861, rho = -0.163934
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.783156
obj = -9.953532, rho = -0.178753
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.678404
obj = -12.078259, rho = -0.099828
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.569448
obj = -14.577558, rho = -0.111785
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.479659
obj = -17.642056, rho = -0.102322
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.412130
obj = -21.106560, rho = -0.030629
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.343715
obj = -25.031728, rho = -0.072950
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.279173
obj = -29.732324, rho = -0.091058
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.241363
obj = -34.805080, rho = -0.222709
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.193889
obj = -39.789381, rho = -0.273276
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.149558
obj = -45.929944, rho = -0.254782
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.119800
obj = -54.250561, rho = -0.234921
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.097994
obj = -64.256354, rho = -0.314386
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.915785, rho = -0.925122
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.302038, rho = -0.892292
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.841317, rho = -0.845067
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.583256, rho = -0.777136
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.580593, rho = -0.679422
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.870567, rho = -0.538864
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 66% (66/100) (classification)
Accuracy = 71.2% (712/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.426839, rho = -0.336679
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 96% (96/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.884166
obj = -8.181575, rho = -0.191739
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.791421
obj = -10.145803, rho = -0.131582
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.680799
obj = -12.441398, rho = -0.079383
nSV = 71, nBSV = 64
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.588063
obj = -15.268084, rho = -0.147591
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.503488
obj = -18.473100, rho = -0.189666
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.428061
obj = -22.245396, rho = -0.265254
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.352780
obj = -26.627462, rho = -0.246054
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.303064
obj = -31.754113, rho = -0.250141
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.251072
obj = -37.254805, rho = -0.260230
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.204100
obj = -43.422103, rho = -0.256646
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.161610
obj = -50.949785, rho = -0.260516
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.137478
obj = -59.979533, rho = -0.489329
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.112319
obj = -69.478880, rho = -0.556235
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -0.786130, rho = 0.942084
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -1.122062, rho = 0.916691
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -1.595930, rho = 0.880164
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -2.258215, rho = 0.827622
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -3.170839, rho = 0.752044
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -4.400754, rho = 0.643327
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -5.998503, rho = 0.486944
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 66% (66/100) (classification)
Accuracy = 59.4% (594/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -7.942085, rho = 0.261994
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 86% (86/100) (classification)
Accuracy = 85.5% (855/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760000
obj = -10.068641, rho = 0.141883
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.673515
obj = -12.479158, rho = 0.157518
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.576801
obj = -15.444075, rho = 0.110978
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.499369
obj = -19.089276, rho = 0.082533
nSV = 50, nBSV = 48
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.435750
obj = -23.324815, rho = 0.094956
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.365821
obj = -28.482391, rho = 0.071710
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.315728
obj = -34.617170, rho = 0.096900
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.266484
obj = -41.764663, rho = 0.138423
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.228012
obj = -49.777988, rho = 0.152316
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.190494
obj = -58.559833, rho = 0.086915
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.153169
obj = -68.631431, rho = 0.170024
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.125193
obj = -81.657804, rho = 0.118984
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.929041, rho = 0.873154
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.316854, rho = 0.817538
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.853828, rho = 0.737537
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.583044, rho = 0.622460
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.542611, rho = 0.456928
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 55% (55/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.737973, rho = 0.218818
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 79% (79/100) (classification)
Accuracy = 78.3% (783/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.944493
obj = -6.078323, rho = -0.039240
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.859341
obj = -7.517406, rho = -0.063085
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.743116
obj = -9.098466, rho = -0.008471
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.616995
obj = -10.902174, rho = -0.064488
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.520000
obj = -13.132549, rho = -0.128158
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.435568
obj = -15.648042, rho = -0.146172
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.360469
obj = -18.572121, rho = -0.191957
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.300913
obj = -22.063603, rho = -0.262043
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.247000
obj = -26.028241, rho = -0.239200
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.204749
obj = -30.288085, rho = -0.187754
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.163425
obj = -35.645150, rho = -0.202684
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.137285
obj = -41.612138, rho = -0.265450
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.108184
obj = -48.553222, rho = -0.346705
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.088683
obj = -57.457900, rho = -0.389329
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.893528, rho = 0.879331
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.268599, rho = 0.826424
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.790270, rho = 0.750320
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.503734, rho = 0.640847
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.453594, rho = 0.483377
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 55% (55/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.661792, rho = 0.256863
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 78% (78/100) (classification)
Accuracy = 73.4% (734/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.082783, rho = 0.022720
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 90.7% (907/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.659503, rho = -0.032580
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.740000
obj = -9.455573, rho = 0.001812
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.649288
obj = -11.523459, rho = -0.033093
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.551056
obj = -13.808134, rho = -0.096244
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.450716
obj = -16.548684, rho = -0.114857
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.374821
obj = -19.839753, rho = -0.107354
nSV = 42, nBSV = 33
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.316353
obj = -23.991019, rho = -0.087749
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.271620
obj = -28.494870, rho = -0.039781
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.220953
obj = -33.760788, rho = -0.030568
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.180361
obj = -40.176567, rho = -0.029514
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.152807
obj = -47.756564, rho = 0.014188
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.127252
obj = -55.703699, rho = 0.177658
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.105434
obj = -63.882491, rho = 0.119779
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.952730, rho = 0.890936
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.353256, rho = 0.843117
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.911005, rho = 0.774331
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -2.675252, rho = 0.675387
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -3.695859, rho = 0.533060
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.2% (472/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -5.001060, rho = 0.328330
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 72% (72/100) (classification)
Accuracy = 66.9% (669/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.541484, rho = 0.033837
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 95% (95/100) (classification)
Accuracy = 91.5% (915/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.889693
obj = -8.271845, rho = -0.011570
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -10.416518, rho = 0.041453
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.694292
obj = -12.981449, rho = -0.057548
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.609553
obj = -15.994606, rho = -0.005789
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.527049
obj = -19.506261, rho = 0.070410
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.445675
obj = -23.472308, rho = 0.087715
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.373730
obj = -28.122415, rho = 0.055103
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.319053
obj = -33.739731, rho = 0.020021
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.261580
obj = -39.903072, rho = -0.010722
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.218365
obj = -46.802148, rho = -0.057269
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.177773
obj = -54.906128, rho = -0.081862
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.141300
obj = -64.966746, rho = -0.092580
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.115518
obj = -78.035559, rho = -0.092592
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.882943, rho = 0.921731
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.259311, rho = 0.887414
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.789197, rho = 0.838051
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.527613, rho = 0.767044
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -3.540546, rho = 0.664904
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.7% (497/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -4.895711, rho = 0.517982
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -6.634229, rho = 0.306641
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 73% (73/100) (classification)
Accuracy = 72.4% (724/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -8.698785, rho = 0.002638
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 93% (93/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -11.059614, rho = 0.006018
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.744163
obj = -13.801607, rho = -0.091111
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.648169
obj = -17.001015, rho = -0.060685
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.560876
obj = -20.803775, rho = -0.004909
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.471062
obj = -25.320092, rho = 0.020010
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.400691
obj = -30.688849, rho = 0.117953
nSV = 42, nBSV = 39
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.340834
obj = -37.075646, rho = 0.083331
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.283748
obj = -44.473701, rho = 0.051946
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.238520
obj = -53.380022, rho = -0.040002
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.203202
obj = -63.516925, rho = -0.070948
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.163504
obj = -75.638972, rho = -0.026375
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.136954
obj = -90.883883, rho = 0.034738
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.947312, rho = -0.886625
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.342046, rho = -0.836915
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.887809, rho = -0.765411
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.627256, rho = -0.662556
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.596549, rho = -0.514603
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 73% (73/100) (classification)
Accuracy = 65.1% (651/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.795574, rho = -0.301781
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 86% (86/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.192604, rho = -0.248110
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 89% (89/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.822614
obj = -7.916436, rho = -0.260496
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 93% (93/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.733700
obj = -10.104346, rho = -0.267356
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 94% (94/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.661548
obj = -12.888955, rho = -0.222473
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.588053
obj = -16.243047, rho = -0.162790
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.524383
obj = -20.310808, rho = -0.148485
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.462087
obj = -24.837882, rho = -0.056306
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.393269
obj = -30.218777, rho = 0.023570
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.331017
obj = -36.457592, rho = 0.086067
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.282864
obj = -43.739272, rho = 0.189390
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.236733
obj = -52.020898, rho = 0.171900
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.194128
obj = -61.702892, rho = 0.191739
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 279
nu = 0.161316
obj = -72.986355, rho = 0.231774
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.133746
obj = -85.908689, rho = 0.343651
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.939296, rho = -0.932011
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.338073, rho = -0.902202
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.897734, rho = -0.859322
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.673892, rho = -0.797642
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.730588, rho = -0.708918
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -5.126923, rho = -0.581292
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.879593, rho = -0.397710
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 87% (87/100) (classification)
Accuracy = 82.3% (823/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.952568
obj = -8.872293, rho = -0.157394
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.847160
obj = -11.059984, rho = -0.180684
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.731957
obj = -13.672489, rho = -0.150510
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.643904
obj = -16.840728, rho = -0.121454
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.546632
obj = -20.612579, rho = -0.159676
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.465291
obj = -25.165038, rho = -0.275177
nSV = 51, nBSV = 41
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.392304
obj = -30.978395, rho = -0.317108
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.336531
obj = -38.268140, rho = -0.297307
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.282682
obj = -47.153923, rho = -0.301355
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.247671
obj = -58.452093, rho = -0.218617
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.210185
obj = -72.561566, rho = -0.140846
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.185229
obj = -90.217611, rho = -0.043248
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.158664
obj = -110.002353, rho = -0.026296
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.955278, rho = 0.892888
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.358528, rho = 0.845924
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.921912, rho = 0.778370
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.697821, rho = 0.681196
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.742557, rho = 0.541417
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.097685, rho = 0.340351
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 72% (72/100) (classification)
Accuracy = 68.7% (687/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.973772
obj = -6.742036, rho = 0.074819
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 95% (95/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.908651
obj = -8.678056, rho = 0.133952
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 95% (95/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.832649
obj = -10.921085, rho = 0.090798
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.719625
obj = -13.611928, rho = 0.057709
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.624570
obj = -16.927928, rho = 0.021454
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.550689
obj = -20.898662, rho = 0.028090
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.477558
obj = -25.622860, rho = 0.039764
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.407593
obj = -31.161446, rho = 0.056911
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.343735
obj = -37.488304, rho = 0.086971
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.290518
obj = -45.123266, rho = 0.100722
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.242507
obj = -53.982775, rho = 0.044094
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.202387
obj = -64.874169, rho = -0.106051
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.168861
obj = -77.355430, rho = -0.076062
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 191
nu = 0.134585
obj = -93.376160, rho = -0.069346
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.957239, rho = -0.919965
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.362585, rho = -0.884873
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.930307, rho = -0.834396
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.715190, rho = -0.761787
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.778495, rho = -0.657342
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.172047, rho = -0.507104
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 62% (62/100) (classification)
Accuracy = 61.6% (616/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.895278, rho = -0.290993
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.940000
obj = -8.853188, rho = -0.121110
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.851304
obj = -11.074874, rho = -0.145114
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.740000
obj = -13.691641, rho = -0.128570
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.645235
obj = -16.828992, rho = -0.085498
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.550157
obj = -20.527465, rho = -0.042903
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.473103
obj = -24.889310, rho = -0.032649
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.396721
obj = -29.859611, rho = 0.003040
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.326600
obj = -35.886884, rho = 0.010966
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.274350
obj = -43.522390, rho = 0.089927
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.227922
obj = -53.062743, rho = 0.106982
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.190329
obj = -65.290069, rho = 0.121311
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.167655
obj = -80.530604, rho = 0.158189
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.143140
obj = -98.284085, rho = -0.022916
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.898324, rho = 0.919502
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.278523, rho = 0.884208
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.810804, rho = 0.833438
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.546222, rho = 0.760410
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.541507, rho = 0.655361
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.843696, rho = 0.504254
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 57% (57/100) (classification)
Accuracy = 52.8% (528/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.448920, rho = 0.286895
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 81% (81/100) (classification)
Accuracy = 80.3% (803/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -8.217808, rho = 0.091258
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.809138
obj = -10.117590, rho = 0.029182
nSV = 83, nBSV = 78
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.708801
obj = -12.163033, rho = -0.076730
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.583713
obj = -14.322042, rho = -0.042213
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.481914
obj = -16.830440, rho = -0.022856
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.392560
obj = -19.721631, rho = 0.002317
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.321646
obj = -23.036328, rho = -0.070502
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.270988
obj = -26.863895, rho = -0.116725
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.221018
obj = -30.394706, rho = -0.076451
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*.*
optimization finished, #iter = 146
nu = 0.178004
obj = -33.686025, rho = -0.005042
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.134146
obj = -36.499389, rho = -0.004611
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.102954
obj = -39.761084, rho = -0.025732
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.....*
optimization finished, #iter = 857
nu = 0.077729
obj = -42.246806, rho = -0.084682
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.900079, rho = -0.941429
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.282154, rho = -0.915749
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.818318, rho = -0.878809
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.561768, rho = -0.825673
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.573674, rho = -0.749240
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.910254, rho = -0.639294
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 57% (57/100) (classification)
Accuracy = 52.6% (526/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.586638, rho = -0.481143
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 80% (80/100) (classification)
Accuracy = 80.4% (804/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -8.560610, rho = -0.346253
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.815834
obj = -10.816699, rho = -0.224305
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.719114
obj = -13.424783, rho = -0.181780
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.631516
obj = -16.560535, rho = -0.201370
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.545183
obj = -20.032098, rho = -0.152589
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.462744
obj = -23.897702, rho = -0.078640
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.390882
obj = -27.998882, rho = -0.120797
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.324546
obj = -32.468567, rho = -0.191401
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.259512
obj = -37.156148, rho = -0.149504
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.213967
obj = -42.284205, rho = -0.165351
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..**.*
optimization finished, #iter = 337
nu = 0.167564
obj = -46.769426, rho = -0.160945
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.
WARNING: using -h 0 may be faster
*
optimization finished, #iter = 190
nu = 0.128158
obj = -52.025667, rho = -0.159707
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*
optimization finished, #iter = 462
nu = 0.097898
obj = -57.724012, rho = -0.192048
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -0.880797, rho = -0.934458
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.254871, rho = -0.905722
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.780010, rho = -0.864385
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.508603, rho = -0.804925
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.501211, rho = -0.719395
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.814321, rho = -0.596363
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 56% (56/100) (classification)
Accuracy = 54.2% (542/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -6.465822, rho = -0.419389
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 79% (79/100) (classification)
Accuracy = 78.5% (785/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.882119
obj = -8.359004, rho = -0.204736
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.795867
obj = -10.526324, rho = -0.106789
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.693050
obj = -13.119371, rho = -0.174548
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.608020
obj = -16.194085, rho = -0.203940
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.540000
obj = -19.795930, rho = -0.116463
nSV = 55, nBSV = 53
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.450346
obj = -23.845866, rho = -0.185105
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.380966
obj = -28.817381, rho = -0.224190
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.320331
obj = -34.569513, rho = -0.196835
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.266776
obj = -41.350509, rho = -0.206971
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.218626
obj = -49.370723, rho = -0.254962
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.177437
obj = -60.122406, rho = -0.271765
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.151566
obj = -74.358148, rho = -0.292864
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 219
nu = 0.127734
obj = -92.110398, rho = -0.323549
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.921010, rho = 0.899436
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.312850, rho = 0.855344
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.863688, rho = 0.791920
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.629545, rho = 0.700687
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.676371, rho = 0.569454
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -5.068745, rho = 0.380681
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 58% (58/100) (classification)
Accuracy = 56.2% (562/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.836896, rho = 0.109140
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 88% (88/100) (classification)
Accuracy = 86.6% (866/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.912063
obj = -8.915511, rho = -0.121596
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.848180
obj = -11.325294, rho = -0.041170
nSV = 87, nBSV = 82
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -14.079227, rho = -0.122101
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.653977
obj = -17.352039, rho = -0.134151
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.556053
obj = -21.267150, rho = -0.172146
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.478571
obj = -26.146583, rho = -0.187169
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.413216
obj = -32.030064, rho = -0.119280
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.350813
obj = -38.778118, rho = -0.008930
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.292692
obj = -47.219122, rho = 0.057686
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.247982
obj = -57.732752, rho = 0.092328
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.209237
obj = -70.658033, rho = 0.070160
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.180529
obj = -86.007374, rho = -0.070492
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 213
nu = 0.149779
obj = -105.514138, rho = -0.063830
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.837118, rho = -0.943279
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.189721, rho = -0.918409
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.681493, rho = -0.882636
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -2.356958, rho = -0.831177
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -3.262524, rho = -0.757157
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -4.428452, rho = -0.650682
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 73% (73/100) (classification)
Accuracy = 66.9% (669/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -5.822768, rho = -0.497524
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 88% (88/100) (classification)
Accuracy = 86.4% (864/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.788684
obj = -7.385683, rho = -0.395343
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.700613
obj = -9.295667, rho = -0.312739
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.615046
obj = -11.601326, rho = -0.235668
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.544837
obj = -14.265995, rho = -0.237682
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.466258
obj = -17.379771, rho = -0.188188
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.399860
obj = -21.123063, rho = -0.106465
nSV = 41, nBSV = 38
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.336979
obj = -25.506411, rho = -0.145083
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.286029
obj = -30.586688, rho = -0.111859
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.236167
obj = -36.336515, rho = -0.180190
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.199384
obj = -43.445116, rho = -0.228392
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.164260
obj = -51.319076, rho = -0.380907
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.132732
obj = -60.793081, rho = -0.391971
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.109334
obj = -72.525043, rho = -0.399724
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.877943, rho = -0.941260
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.248965, rho = -0.915505
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.767789, rho = -0.878458
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.483316, rho = -0.825168
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.448890, rho = -0.748513
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.706062, rho = -0.638248
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 64% (64/100) (classification)
Accuracy = 59.4% (594/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.241818, rho = -0.479638
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 94% (94/100) (classification)
Accuracy = 88.4% (884/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.857819
obj = -7.982330, rho = -0.339454
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 95% (95/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -9.926005, rho = -0.242536
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.681255
obj = -12.025180, rho = -0.263621
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.565046
obj = -14.542576, rho = -0.262518
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.483110
obj = -17.404994, rho = -0.393813
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.399914
obj = -20.750338, rho = -0.437427
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.327199
obj = -24.950746, rho = -0.457631
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.271891
obj = -30.257378, rho = -0.450201
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.222643
obj = -37.291132, rho = -0.439082
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.193532
obj = -46.480452, rho = -0.485259
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.171960
obj = -57.402823, rho = -0.413825
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.143757
obj = -69.957357, rho = -0.385108
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.124656
obj = -85.760754, rho = -0.364702
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -0.843558, rho = -0.950245
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.203045, rho = -0.928429
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -1.709063, rho = -0.897049
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -2.414004, rho = -0.851910
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.380559, rho = -0.786980
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -4.672683, rho = -0.693582
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -6.328115, rho = -0.559233
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 69% (69/100) (classification)
Accuracy = 66.6% (666/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.288874, rho = -0.365978
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 91.5% (915/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780941
obj = -10.453293, rho = -0.250221
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.694776
obj = -13.067108, rho = -0.188717
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.620000
obj = -16.053699, rho = -0.135395
nSV = 63, nBSV = 61
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.523024
obj = -19.425510, rho = -0.149631
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.444284
obj = -23.462438, rho = -0.190228
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.378017
obj = -28.337363, rho = -0.287253
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.311940
obj = -33.878240, rho = -0.289459
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.260673
obj = -40.888282, rho = -0.276249
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.218234
obj = -49.216618, rho = -0.399748
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.182152
obj = -58.998634, rho = -0.567085
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.155000
obj = -70.697448, rho = -0.569335
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.125725
obj = -84.911595, rho = -0.587408
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.971665, rho = -0.034893
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.379820, rho = -0.050193
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.947826, rho = -0.072199
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.725339, rho = -0.103855
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.761953, rho = -0.149391
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.083815, rho = -0.214891
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -6.646803, rho = -0.243706
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.915207
obj = -8.433919, rho = -0.239469
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.817165
obj = -10.458853, rho = -0.202435
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.705189
obj = -12.824488, rho = -0.148819
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.588111
obj = -15.743604, rho = -0.125256
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.508115
obj = -19.355446, rho = -0.101190
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.440202
obj = -23.665138, rho = -0.047092
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.377116
obj = -28.826439, rho = -0.060358
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.320060
obj = -34.927240, rho = -0.089833
nSV = 34, nBSV = 31
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.280882
obj = -41.618730, rho = -0.086175
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.226672
obj = -48.519336, rho = -0.137814
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.182664
obj = -56.986034, rho = -0.177604
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.154141
obj = -67.201238, rho = -0.119410
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.126497
obj = -76.490376, rho = -0.143743
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -0.780790, rho = -0.950528
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -1.111013, rho = -0.928837
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -1.573068, rho = -0.897635
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -2.210910, rho = -0.852753
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -3.072959, rho = -0.788193
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -4.198228, rho = -0.695326
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 61% (61/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -5.579448, rho = -0.561742
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 78% (78/100) (classification)
Accuracy = 70% (700/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -7.084490, rho = -0.392471
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 95% (95/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.705322
obj = -8.595173, rho = -0.267463
nSV = 73, nBSV = 67
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.591323
obj = -10.259283, rho = -0.206339
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.500830
obj = -12.071300, rho = -0.116099
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.400416
obj = -14.182011, rho = -0.124107
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.329950
obj = -16.686902, rho = -0.151731
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.267608
obj = -19.703166, rho = -0.102423
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.217018
obj = -23.487917, rho = -0.106592
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.178512
obj = -28.327650, rho = -0.097260
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.147888
obj = -34.394248, rho = -0.117084
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.123838
obj = -42.137893, rho = -0.158460
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.105610
obj = -52.185731, rho = -0.143924
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.097746
obj = -63.520068, rho = -0.289007
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -0.880155, rho = -0.947031
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.253542, rho = -0.923807
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.777260, rho = -0.890400
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.502913, rho = -0.842346
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.489439, rho = -0.773223
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.789963, rho = -0.673793
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 58% (58/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -6.415422, rho = -0.530768
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 77% (77/100) (classification)
Accuracy = 76.3% (763/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.868821
obj = -8.279919, rho = -0.393883
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 91% (91/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.801103
obj = -10.396195, rho = -0.312776
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 95% (95/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.697596
obj = -12.690808, rho = -0.237866
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.604769
obj = -15.397822, rho = -0.178384
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.513198
obj = -18.468822, rho = -0.135055
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.426440
obj = -21.874685, rho = -0.066328
nSV = 47, nBSV = 37
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.343229
obj = -26.177693, rho = -0.036304
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.285539
obj = -31.760154, rho = -0.031344
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.243704
obj = -38.468755, rho = -0.010067
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*..*
optimization finished, #iter = 287
nu = 0.202155
obj = -46.547360, rho = 0.036236
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.170836
obj = -57.031218, rho = 0.115008
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.147856
obj = -69.541057, rho = 0.171433
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*..*
optimization finished, #iter = 462
nu = 0.122889
obj = -83.925114, rho = 0.171822
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.947092, rho = 0.864706
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.341589, rho = 0.805386
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.886864, rho = 0.720057
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.625300, rho = 0.597317
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.592502, rho = 0.420760
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 54% (54/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.787201, rho = 0.166792
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 88% (880/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.956254
obj = -6.112864, rho = -0.111679
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.848423
obj = -7.566553, rho = -0.061049
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.733530
obj = -9.285254, rho = -0.058848
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.635244
obj = -11.284750, rho = -0.037478
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.540000
obj = -13.569812, rho = -0.001703
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.455510
obj = -16.098487, rho = 0.064293
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.372820
obj = -19.062505, rho = 0.095492
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.314452
obj = -22.387485, rho = 0.027258
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.249160
obj = -26.266665, rho = -0.034776
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.204768
obj = -31.044943, rho = -0.124329
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.170510
obj = -36.766042, rho = -0.230032
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.138557
obj = -43.160510, rho = -0.240070
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.116790
obj = -50.160586, rho = -0.098169
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.094144
obj = -57.128686, rho = -0.135766
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.928878, rho = -0.890993
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.316517, rho = -0.843198
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.853130, rho = -0.774448
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.581600, rho = -0.675555
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.539624, rho = -0.533303
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 55% (55/100) (classification)
Accuracy = 54.4% (544/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.731793, rho = -0.328679
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 87% (87/100) (classification)
Accuracy = 84% (840/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -6.093349, rho = -0.151561
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.848567
obj = -7.628670, rho = 0.014185
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.750636
obj = -9.343206, rho = 0.015380
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.642278
obj = -11.182058, rho = 0.056740
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.541496
obj = -13.284764, rho = 0.089326
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.440710
obj = -15.732433, rho = 0.095671
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.360027
obj = -18.754179, rho = 0.069865
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.297735
obj = -22.454169, rho = 0.071348
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.246063
obj = -27.200847, rho = 0.062718
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.209253
obj = -33.048035, rho = 0.141719
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.173483
obj = -40.155710, rho = 0.125625
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.144689
obj = -49.401525, rho = 0.252229
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.122569
obj = -61.499544, rho = 0.271078
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.105988
obj = -77.040578, rho = 0.212108
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.876751, rho = 0.899711
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.246501, rho = 0.855739
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.762690, rho = 0.792488
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.472765, rho = 0.701505
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.427058, rho = 0.570630
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.6% (496/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.660890, rho = 0.382372
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 64% (64/100) (classification)
Accuracy = 59.9% (599/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.148351, rho = 0.111573
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 91% (91/100) (classification)
Accuracy = 86.1% (861/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.819657, rho = -0.042414
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 95% (95/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.755387
obj = -9.717603, rho = -0.059593
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.659264
obj = -11.883435, rho = -0.007560
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.563138
obj = -14.334325, rho = -0.033191
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.476932
obj = -17.162129, rho = -0.056769
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.397153
obj = -20.526698, rho = -0.063345
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.329504
obj = -24.441768, rho = -0.104745
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.269755
obj = -29.187356, rho = -0.105400
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.226054
obj = -34.929636, rho = -0.045834
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.184963
obj = -42.114580, rho = -0.042170
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.153245
obj = -51.143361, rho = 0.003163
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.130458
obj = -62.877815, rho = 0.063140
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.112061
obj = -76.193762, rho = 0.069318
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.936711, rho = 0.880084
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.332723, rho = 0.827507
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.886663, rho = 0.751877
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.650984, rho = 0.643087
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.683188, rho = 0.486599
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51% (510/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -5.028846, rho = 0.261499
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 64% (64/100) (classification)
Accuracy = 68.4% (684/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.676657, rho = -0.062297
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.903783
obj = -8.566348, rho = -0.153099
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.828782
obj = -10.701867, rho = -0.148336
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.729797
obj = -13.130215, rho = -0.183562
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.634412
obj = -15.854085, rho = -0.209266
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.518974
obj = -18.952613, rho = -0.224918
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.435499
obj = -22.932768, rho = -0.308770
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.369575
obj = -27.478641, rho = -0.285474
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.309284
obj = -32.478193, rho = -0.245630
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.256571
obj = -38.189838, rho = -0.246428
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.212368
obj = -44.136692, rho = -0.361806
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.169342
obj = -50.979701, rho = -0.383715
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.139242
obj = -58.444871, rho = -0.459630
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.109390
obj = -65.568548, rho = -0.501805
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.963505, rho = -0.016612
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.362936, rho = -0.023895
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.912890, rho = -0.034372
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.653053, rho = -0.049442
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.612383, rho = -0.071120
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.774333, rho = -0.102302
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.934462
obj = -6.107881, rho = -0.139249
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.844039
obj = -7.653092, rho = -0.054926
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.745279
obj = -9.425069, rho = -0.001125
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.640979
obj = -11.404858, rho = -0.051697
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.534743
obj = -13.823681, rho = -0.054206
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.455641
obj = -16.697880, rho = -0.047898
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.380637
obj = -20.183818, rho = -0.048720
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.326436
obj = -24.034413, rho = -0.010645
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.263444
obj = -28.637449, rho = 0.062190
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.220349
obj = -34.220996, rho = 0.146813
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 203
nu = 0.183565
obj = -40.787562, rho = 0.218685
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.153870
obj = -48.486767, rho = 0.209750
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.123973
obj = -58.091817, rho = 0.226184
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.103650
obj = -69.975861, rho = 0.292737
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.966891, rho = -0.004024
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.369944, rho = -0.005789
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.927389, rho = -0.008327
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.683053, rho = -0.011978
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.674457, rho = -0.017230
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.902773, rho = -0.024784
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.945937
obj = -6.312233, rho = -0.028563
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.855683
obj = -7.979172, rho = -0.021904
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -9.960451, rho = -0.071358
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.672407
obj = -12.193119, rho = -0.065701
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.570898
obj = -14.812089, rho = -0.027290
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.476169
obj = -18.122417, rho = -0.086403
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.407647
obj = -22.159636, rho = -0.096328
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.352204
obj = -26.987359, rho = -0.082916
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.303077
obj = -32.399482, rho = -0.197928
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.247158
obj = -38.733064, rho = -0.142863
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.207832
obj = -47.003675, rho = -0.204734
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.172984
obj = -56.873638, rho = -0.260664
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.150210
obj = -68.121228, rho = -0.187301
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.127345
obj = -78.654614, rho = -0.262010
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.949947, rho = -0.900229
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.347497, rho = -0.856484
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.899088, rho = -0.793559
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.650595, rho = -0.703045
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.644840, rho = -0.572846
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.895496, rho = -0.385560
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 84% (84/100) (classification)
Accuracy = 83.9% (839/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.323056, rho = -0.126608
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.881814
obj = -7.846910, rho = -0.075155
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.774281
obj = -9.510207, rho = -0.095614
nSV = 81, nBSV = 73
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.650094
obj = -11.411708, rho = -0.065542
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.539526
obj = -13.672119, rho = -0.058481
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.444339
obj = -16.483065, rho = -0.108290
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.378110
obj = -19.998684, rho = -0.072748
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.313543
obj = -24.148380, rho = -0.055729
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.266155
obj = -29.404215, rho = -0.166820
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.226122
obj = -35.506140, rho = -0.137601
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.192739
obj = -42.623344, rho = -0.159065
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.163646
obj = -50.514154, rho = -0.243604
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.134559
obj = -59.194401, rho = -0.433605
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.110689
obj = -67.979693, rho = -0.413395
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -0.838189, rho = 0.921429
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.191936, rho = 0.886979
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -1.686077, rho = 0.837425
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -2.366442, rho = 0.766144
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -3.282146, rho = 0.663609
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -4.469053, rho = 0.516119
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 62% (62/100) (classification)
Accuracy = 54.1% (541/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -5.906778, rho = 0.303961
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 92% (92/100) (classification)
Accuracy = 83.2% (832/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.814978
obj = -7.478887, rho = 0.130952
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.733752
obj = -9.232791, rho = 0.100942
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.620000
obj = -11.224332, rho = 0.062686
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.524849
obj = -13.663945, rho = 0.071935
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.445040
obj = -16.654942, rho = 0.117694
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.373581
obj = -20.370548, rho = 0.159747
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.320000
obj = -24.981003, rho = 0.175466
nSV = 34, nBSV = 31
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.269685
obj = -30.380930, rho = 0.175216
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.224279
obj = -37.534368, rho = 0.115781
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.196372
obj = -46.769973, rho = 0.174642
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.174676
obj = -57.308137, rho = 0.175825
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.149785
obj = -69.460636, rho = 0.145997
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.124975
obj = -83.361349, rho = 0.116005
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.931792, rho = -0.925485
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.322547, rho = -0.893126
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.865607, rho = -0.846267
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.607416, rho = -0.778862
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -3.593040, rho = -0.681905
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -4.842318, rho = -0.542436
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 78% (78/100) (classification)
Accuracy = 70.3% (703/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -6.290705, rho = -0.341817
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 92.5% (925/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.875078
obj = -7.871728, rho = -0.310227
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.762719
obj = -9.684307, rho = -0.270986
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.643017
obj = -11.810216, rho = -0.229245
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.553219
obj = -14.495687, rho = -0.167749
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.477866
obj = -17.710874, rho = -0.090688
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.413668
obj = -21.302333, rho = -0.033494
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.342261
obj = -25.381009, rho = -0.008122
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.281240
obj = -30.264541, rho = -0.066123
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.234043
obj = -36.398844, rho = -0.052576
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.198148
obj = -43.417296, rho = -0.068024
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.167294
obj = -50.923861, rho = -0.004588
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.137843
obj = -58.984261, rho = 0.015181
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.114689
obj = -66.894080, rho = -0.113624
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.939650, rho = -0.926923
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.338804, rho = -0.894882
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.899246, rho = -0.848793
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.677021, rho = -0.782496
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.737062, rho = -0.687132
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -5.140318, rho = -0.549954
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 52.7% (527/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.907310, rho = -0.352632
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 84% (84/100) (classification)
Accuracy = 86.8% (868/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -8.944913, rho = -0.196641
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.866915
obj = -11.129155, rho = -0.206191
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.759410
obj = -13.548087, rho = -0.135754
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.650605
obj = -16.232500, rho = -0.144882
nSV = 68, nBSV = 61
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.541723
obj = -19.323680, rho = -0.161045
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.445701
obj = -22.952218, rho = -0.181372
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.375377
obj = -27.152573, rho = -0.141366
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.307249
obj = -31.851661, rho = -0.134860
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.251124
obj = -37.151615, rho = -0.168811
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.202007
obj = -43.182194, rho = -0.217727
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.168285
obj = -50.073241, rho = -0.304019
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 227
nu = 0.134066
obj = -57.495833, rho = -0.378467
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.106340
obj = -66.131483, rho = -0.410176
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.931279, rho = 0.851056
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.321483, rho = 0.785752
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.863407, rho = 0.691815
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.602865, rho = 0.556691
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.583622, rho = 0.362322
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 56% (56/100) (classification)
Accuracy = 52.9% (529/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.822831, rho = 0.082732
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 82% (82/100) (classification)
Accuracy = 83.2% (832/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.263971, rho = -0.234608
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 97% (97/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.852446
obj = -7.940830, rho = -0.185824
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.764851
obj = -9.930557, rho = -0.161809
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.662807
obj = -12.194664, rho = -0.138285
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.578805
obj = -14.747426, rho = 0.011272
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.502077
obj = -17.618718, rho = -0.033949
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.415730
obj = -20.523705, rho = -0.053471
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.337552
obj = -23.832760, rho = -0.111155
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.270751
obj = -27.371849, rho = -0.145996
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.217260
obj = -31.584753, rho = -0.186480
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.174624
obj = -36.119933, rho = -0.282057
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.140158
obj = -41.633956, rho = -0.300002
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.115807
obj = -47.139123, rho = -0.464232
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.091962
obj = -52.066404, rho = -0.493397
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -0.895030, rho = -0.927048
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.271707, rho = -0.895062
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -1.796701, rho = -0.849052
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -2.517040, rho = -0.782870
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.5% (545/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -3.481125, rho = -0.687669
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 54.6% (546/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -4.718758, rho = -0.550727
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 70% (70/100) (classification)
Accuracy = 73.6% (736/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.918408
obj = -6.190440, rho = -0.357597
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 92% (92/100) (classification)
Accuracy = 91.9% (919/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.889654, rho = -0.347996
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 93% (93/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.746678
obj = -9.927984, rho = -0.286786
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.662354
obj = -12.322103, rho = -0.249891
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.577085
obj = -15.074882, rho = -0.317195
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.490809
obj = -18.342535, rho = -0.285423
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.415335
obj = -22.279301, rho = -0.317181
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.346142
obj = -27.195769, rho = -0.264644
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 184
nu = 0.293234
obj = -33.401536, rho = -0.224771
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.258519
obj = -41.107129, rho = -0.150896
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.216984
obj = -50.167448, rho = -0.149716
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.191264
obj = -60.331367, rho = -0.132429
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.152059
obj = -72.287647, rho = -0.137905
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.125542
obj = -89.045618, rho = -0.124171
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.898241, rho = -0.936954
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.278351, rho = -0.909312
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.810449, rho = -0.869550
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.545487, rho = -0.812354
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.539987, rho = -0.730080
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.840551, rho = -0.611734
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 69% (69/100) (classification)
Accuracy = 56.6% (566/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.442414, rho = -0.441499
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 85% (85/100) (classification)
Accuracy = 86.8% (868/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.866466
obj = -8.293646, rho = -0.353522
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 93% (93/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.780000
obj = -10.501877, rho = -0.298746
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 95% (95/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.682457
obj = -13.271112, rho = -0.279670
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.614504
obj = -16.717141, rho = -0.210518
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.541996
obj = -20.654385, rho = -0.214456
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.465115
obj = -25.352293, rho = -0.218795
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.399299
obj = -30.971879, rho = -0.178904
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.340995
obj = -37.716352, rho = -0.149842
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.286472
obj = -45.950769, rho = -0.136406
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.242840
obj = -55.489516, rho = -0.158553
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.208668
obj = -66.922415, rho = -0.206020
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.174842
obj = -79.864720, rho = -0.214306
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.144281
obj = -94.291649, rho = -0.290308
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.949906, rho = -0.889152
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.347412, rho = -0.840551
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.898912, rho = -0.770641
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.650230, rho = -0.670078
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.644085, rho = -0.525424
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 64% (64/100) (classification)
Accuracy = 63.5% (635/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.893932, rho = -0.317347
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 90% (90/100) (classification)
Accuracy = 91.8% (918/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.945547
obj = -6.352721, rho = -0.157966
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.844070
obj = -8.125674, rho = -0.113935
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.776280
obj = -10.322343, rho = -0.087346
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.702533
obj = -12.788137, rho = -0.106665
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.598321
obj = -15.616525, rho = -0.139117
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.501206
obj = -19.139039, rho = -0.178610
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.432466
obj = -23.494397, rho = -0.163491
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.365053
obj = -28.762664, rho = -0.100715
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.306842
obj = -35.655289, rho = -0.096325
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.266746
obj = -44.126435, rho = -0.164451
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.225756
obj = -54.976886, rho = -0.177582
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.198029
obj = -69.004159, rho = -0.311603
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.168585
obj = -86.685847, rho = -0.370869
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.151659
obj = -109.104478, rho = -0.504531
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.951208, rho = -0.921244
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.350106, rho = -0.886713
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.904487, rho = -0.837043
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.661765, rho = -0.765594
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.667952, rho = -0.662819
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -4.943317, rho = -0.514982
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 75% (75/100) (classification)
Accuracy = 71.5% (715/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -6.422006, rho = -0.302326
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 92% (92/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.885314
obj = -8.019684, rho = -0.259368
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.770935
obj = -9.901794, rho = -0.202797
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.681147
obj = -12.024740, rho = -0.104511
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.573673
obj = -14.326092, rho = -0.044505
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.478995
obj = -17.015943, rho = 0.003255
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.391440
obj = -20.130985, rho = -0.012495
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.327387
obj = -23.726391, rho = 0.068637
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.267283
obj = -27.849652, rho = 0.126411
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.221954
obj = -32.350842, rho = 0.148990
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.181807
obj = -36.644294, rho = 0.243591
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.145025
obj = -41.074421, rho = 0.322483
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.111385
obj = -46.118560, rho = 0.333614
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.086141
obj = -51.505747, rho = 0.285186
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.915067, rho = -0.927143
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.300553, rho = -0.895198
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.838243, rho = -0.849248
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.576896, rho = -0.783151
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.567432, rho = -0.688074
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.843335, rho = -0.551310
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 59% (59/100) (classification)
Accuracy = 59.7% (597/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.370491, rho = -0.354581
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.013889, rho = -0.205307
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.790516
obj = -9.832694, rho = -0.125334
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.667313
obj = -11.881710, rho = -0.102403
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.560026
obj = -14.381572, rho = -0.068720
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.467272
obj = -17.435431, rho = -0.021522
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.404744
obj = -21.084614, rho = 0.055836
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.345238
obj = -25.065998, rho = 0.096722
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.279725
obj = -29.446472, rho = 0.110329
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.228752
obj = -34.920170, rho = 0.047493
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.190497
obj = -41.142966, rho = -0.022384
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.157540
obj = -48.157074, rho = 0.092680
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.134283
obj = -55.492609, rho = 0.225306
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
...*.*
optimization finished, #iter = 453
nu = 0.101535
obj = -63.001325, rho = 0.204817
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.891284, rho = 0.863639
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.263957, rho = 0.803851
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.780666, rho = 0.717849
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.483862, rho = 0.594140
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.412475, rho = 0.416191
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 58% (58/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.576711, rho = 0.160221
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 83% (83/100) (classification)
Accuracy = 78.2% (782/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.883987
obj = -5.919886, rho = -0.032948
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -7.505742, rho = -0.042430
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.718221
obj = -9.375938, rho = -0.011067
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.632576
obj = -11.576731, rho = -0.063332
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.540106
obj = -14.160202, rho = -0.022071
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.476413
obj = -17.237314, rho = -0.090018
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.394106
obj = -20.695464, rho = -0.140796
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.325118
obj = -24.972128, rho = -0.103011
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.269361
obj = -30.588568, rho = -0.075502
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.227210
obj = -37.759514, rho = -0.053697
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.200368
obj = -46.229214, rho = 0.061876
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.170381
obj = -56.123840, rho = 0.198826
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.141633
obj = -68.532037, rho = 0.339913
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.120956
obj = -84.117529, rho = 0.381944
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.875439, rho = 0.909112
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.243785, rho = 0.869262
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -1.757070, rho = 0.811940
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -2.461138, rho = 0.729486
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.402999, rho = 0.610879
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.1% (501/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.611108, rho = 0.440269
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 69% (69/100) (classification)
Accuracy = 64.2% (642/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.045346, rho = 0.194855
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.829081
obj = -7.656444, rho = 0.135370
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -9.500609, rho = 0.126590
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.652076
obj = -11.597637, rho = 0.104072
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.560000
obj = -13.933556, rho = 0.112217
nSV = 57, nBSV = 55
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.470991
obj = -16.391865, rho = 0.139120
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.380829
obj = -19.303232, rho = 0.140305
nSV = 40, nBSV = 37
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.313037
obj = -22.597159, rho = 0.130804
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.255013
obj = -26.635304, rho = 0.107360
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.211240
obj = -31.096473, rho = 0.146959
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.173751
obj = -35.630152, rho = 0.133895
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.138166
obj = -40.714373, rho = 0.176439
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 244
nu = 0.108953
obj = -46.194601, rho = 0.218004
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.084621
obj = -53.140301, rho = 0.154773
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.872751, rho = -0.939286
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.238223, rho = -0.912666
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.745561, rho = -0.874374
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -2.437325, rho = -0.819293
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -3.353726, rho = -0.740062
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 57% (57/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -4.509156, rho = -0.626093
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 78% (78/100) (classification)
Accuracy = 70.8% (708/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -5.849392, rho = -0.498473
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 88.5% (885/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.796330
obj = -7.395333, rho = -0.424656
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.700000
obj = -9.266956, rho = -0.373476
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.624029
obj = -11.466227, rho = -0.310073
nSV = 64, nBSV = 62
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.540000
obj = -14.002041, rho = -0.335569
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.467658
obj = -16.873926, rho = -0.278265
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.387882
obj = -20.046479, rho = -0.318341
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.324770
obj = -23.831563, rho = -0.273169
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.270994
obj = -28.139212, rho = -0.261471
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.221742
obj = -32.859242, rho = -0.324319
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.186792
obj = -37.992358, rho = -0.307263
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.143633
obj = -43.490172, rho = -0.341446
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.116764
obj = -49.982967, rho = -0.357124
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.095385
obj = -56.829682, rho = -0.285695
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.921385, rho = 0.934043
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.313625, rho = 0.905124
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.865291, rho = 0.863526
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.632862, rho = 0.803689
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.683234, rho = 0.717616
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.1% (471/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -5.082946, rho = 0.593805
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.4% (474/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.866279, rho = 0.415710
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 70% (70/100) (classification)
Accuracy = 69.3% (693/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -8.955446, rho = 0.162089
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.871348
obj = -11.223031, rho = 0.050345
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.743688
obj = -13.861120, rho = 0.114763
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.640000
obj = -17.095137, rho = 0.152792
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.555093
obj = -20.964263, rho = 0.148381
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.484651
obj = -25.575901, rho = 0.026967
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.398504
obj = -31.054282, rho = 0.074504
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.339987
obj = -38.038056, rho = 0.217724
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.292295
obj = -46.455012, rho = 0.127720
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 216
nu = 0.247301
obj = -55.868257, rho = 0.084745
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.205511
obj = -67.113967, rho = 0.107154
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.173907
obj = -81.361840, rho = 0.114424
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.143549
obj = -97.742445, rho = 0.145008
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.950012, rho = -0.899875
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.347632, rho = -0.855975
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.899368, rho = -0.792827
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.651173, rho = -0.701992
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.646036, rho = -0.571330
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 58% (58/100) (classification)
Accuracy = 53.3% (533/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.897970, rho = -0.383380
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 88% (88/100) (classification)
Accuracy = 84.6% (846/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.950681
obj = -6.353097, rho = -0.226288
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.849791
obj = -8.098962, rho = -0.226946
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.773340
obj = -10.200858, rho = -0.109584
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.680000
obj = -12.671577, rho = -0.064882
nSV = 69, nBSV = 67
Total nSV = 69
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.592459
obj = -15.529936, rho = -0.144155
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.512083
obj = -18.947273, rho = -0.101710
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.431262
obj = -22.903172, rho = -0.144148
nSV = 48, nBSV = 38
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.365972
obj = -27.774512, rho = -0.110572
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.311102
obj = -33.077392, rho = -0.082547
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.265941
obj = -38.795729, rho = -0.111710
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.210963
obj = -44.919629, rho = -0.182001
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.174943
obj = -52.242152, rho = -0.280049
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.139829
obj = -60.469920, rho = -0.236661
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.111684
obj = -69.624457, rho = -0.231737
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.894025, rho = 0.881869
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.269628, rho = 0.830075
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.792400, rho = 0.755571
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.508141, rho = 0.648401
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.2% (502/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.462712, rho = 0.494242
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.680657, rho = 0.272493
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 74% (74/100) (classification)
Accuracy = 69.6% (696/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.917776
obj = -6.111637, rho = -0.039577
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -7.743208, rho = -0.123843
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.741349
obj = -9.627623, rho = -0.222567
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.650299
obj = -11.851282, rho = -0.171060
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.561422
obj = -14.342692, rho = -0.220299
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.471958
obj = -17.365215, rho = -0.161053
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.402786
obj = -20.851091, rho = -0.162340
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.339526
obj = -24.839626, rho = -0.124389
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.279179
obj = -29.378438, rho = -0.058816
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.226134
obj = -34.701500, rho = 0.046535
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.191179
obj = -41.226868, rho = -0.009390
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.155844
obj = -48.028784, rho = -0.082284
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.126040
obj = -56.254621, rho = -0.069299
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*...*
optimization finished, #iter = 307
nu = 0.102953
obj = -65.738052, rho = -0.240979
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.950395, rho = 0.856208
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.348424, rho = 0.793162
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.901006, rho = 0.702474
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.654562, rho = 0.572023
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.8% (518/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.653049, rho = 0.384377
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 58% (58/100) (classification)
Accuracy = 55.7% (557/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.912481, rho = 0.114457
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 83% (83/100) (classification)
Accuracy = 85.1% (851/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.951583
obj = -6.391747, rho = -0.121054
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.850982
obj = -8.169018, rho = -0.189347
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -10.418442, rho = -0.179543
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.688315
obj = -13.113553, rho = -0.129413
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.607036
obj = -16.391978, rho = -0.078887
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.529410
obj = -20.198589, rho = -0.191893
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.457981
obj = -24.762039, rho = -0.255682
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.390635
obj = -29.978924, rho = -0.142048
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.328621
obj = -36.603140, rho = -0.107857
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.276209
obj = -44.437179, rho = -0.126717
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.230052
obj = -54.363162, rho = -0.176058
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.194889
obj = -67.339997, rho = -0.258677
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.170039
obj = -83.540052, rho = -0.427223
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.140885
obj = -104.553179, rho = -0.388460
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.894996, rho = 0.900545
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.271638, rho = 0.856939
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.796558, rho = 0.794215
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -2.516743, rho = 0.703988
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.480511, rho = 0.574202
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.1% (511/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.717488, rho = 0.387510
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 70% (70/100) (classification)
Accuracy = 62.5% (625/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.187781, rho = 0.119055
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 88% (88/100) (classification)
Accuracy = 88.6% (886/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.842682, rho = 0.071106
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.756533
obj = -9.661741, rho = 0.111522
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.656814
obj = -11.770656, rho = 0.082862
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.560000
obj = -14.168873, rho = 0.063396
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.469615
obj = -16.857171, rho = 0.146649
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.379977
obj = -20.265066, rho = 0.124401
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.323949
obj = -24.446249, rho = 0.068747
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.274970
obj = -29.068054, rho = 0.078093
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.221728
obj = -34.530136, rho = 0.109740
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.189057
obj = -41.295237, rho = 0.133802
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.156419
obj = -48.947999, rho = 0.057245
nSV = 17, nBSV = 13
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.126441
obj = -58.108074, rho = 0.121181
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 179
nu = 0.100912
obj = -70.247041, rho = 0.129090
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.943674, rho = 0.821394
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.334518, rho = 0.743085
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.872233, rho = 0.630440
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.595027, rho = 0.468407
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 47.9% (479/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.529862, rho = 0.235330
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 68% (68/100) (classification)
Accuracy = 62.8% (628/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.977982
obj = -4.657624, rho = -0.090482
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 95% (95/100) (classification)
Accuracy = 89.6% (896/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900479
obj = -5.986929, rho = -0.141042
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.823283
obj = -7.517308, rho = -0.163721
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.731411
obj = -9.230628, rho = -0.162137
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.634250
obj = -11.161766, rho = -0.167709
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.531027
obj = -13.341152, rho = -0.205233
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.449671
obj = -15.767525, rho = -0.257215
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.373784
obj = -18.447042, rho = -0.321348
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.299544
obj = -21.384573, rho = -0.335329
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.237926
obj = -25.102851, rho = -0.369117
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.195355
obj = -29.750504, rho = -0.320902
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.162177
obj = -34.725535, rho = -0.235136
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.133155
obj = -40.612878, rho = -0.295993
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.109836
obj = -46.811414, rho = -0.419770
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.093606
obj = -51.971065, rho = -0.359067
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.914841, rho = -0.939630
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.300086, rho = -0.913161
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.837277, rho = -0.875087
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.574898, rho = -0.820319
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.563298, rho = -0.741537
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.834781, rho = -0.628215
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 77% (77/100) (classification)
Accuracy = 69.3% (693/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.352793, rho = -0.465205
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 92% (92/100) (classification)
Accuracy = 89.3% (893/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.870163
obj = -8.038626, rho = -0.378909
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -10.070072, rho = -0.361581
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.687270
obj = -12.441415, rho = -0.273824
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.590508
obj = -15.080344, rho = -0.226608
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.494515
obj = -18.174051, rho = -0.223096
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.416032
obj = -21.904454, rho = -0.162680
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.342889
obj = -26.366473, rho = -0.176998
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.284781
obj = -32.044295, rho = -0.231647
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.248320
obj = -38.806606, rho = -0.111883
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 308
nu = 0.209756
obj = -46.329423, rho = -0.137759
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.167727
obj = -55.865883, rho = -0.122417
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.144356
obj = -67.705196, rho = -0.168593
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.123913
obj = -81.086968, rho = -0.285851
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -0.897375, rho = 0.904998
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.276559, rho = 0.863344
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.806741, rho = 0.803427
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -2.537814, rho = 0.717240
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -3.524109, rho = 0.593264
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -4.807697, rho = 0.414930
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 65% (65/100) (classification)
Accuracy = 57.1% (571/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -6.374434, rho = 0.158406
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 88% (88/100) (classification)
Accuracy = 91% (910/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.868048
obj = -8.195965, rho = 0.043433
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.794246
obj = -10.236652, rho = 0.022763
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.690048
obj = -12.637731, rho = -0.044781
nSV = 72, nBSV = 65
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.594184
obj = -15.358254, rho = -0.140379
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.504072
obj = -18.549196, rho = -0.202264
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.430204
obj = -22.424638, rho = -0.195911
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.363584
obj = -26.814081, rho = -0.290177
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.301445
obj = -31.579759, rho = -0.305139
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.245488
obj = -37.287010, rho = -0.325240
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*....*
optimization finished, #iter = 440
nu = 0.200370
obj = -44.184972, rho = -0.324058
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.....*
optimization finished, #iter = 567
nu = 0.163939
obj = -52.530975, rho = -0.294961
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.139438
obj = -62.215292, rho = -0.218436
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.115721
obj = -73.346189, rho = -0.221435
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.931391, rho = -0.924534
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.321715, rho = -0.891446
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.863887, rho = -0.843850
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.603857, rho = -0.775386
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.585676, rho = -0.676905
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 55% (55/100) (classification)
Accuracy = 54.2% (542/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.827080, rho = -0.535243
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 79% (79/100) (classification)
Accuracy = 79.6% (796/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.936055
obj = -6.281207, rho = -0.378907
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.866924
obj = -7.941247, rho = -0.299430
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.762074
obj = -9.872505, rho = -0.254485
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.663889
obj = -12.141829, rho = -0.232820
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.578622
obj = -14.703317, rho = -0.196517
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.490004
obj = -17.643032, rho = -0.157547
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.404608
obj = -21.056167, rho = -0.086054
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.339748
obj = -24.951245, rho = -0.109120
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.274091
obj = -29.764789, rho = -0.111837
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.233690
obj = -35.260287, rho = -0.294211
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.189861
obj = -41.528411, rho = -0.435577
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.158018
obj = -49.175318, rho = -0.617867
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.137577
obj = -56.267353, rho = -0.875382
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.108732
obj = -62.665296, rho = -0.842385
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -0.879197, rho = -0.930365
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -1.251561, rho = -0.899833
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.773166, rho = -0.856113
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.494443, rho = -0.793025
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -3.471913, rho = -0.702277
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -4.753701, rho = -0.571741
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 59% (59/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -6.340389, rho = -0.383971
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 89% (89/100) (classification)
Accuracy = 82.9% (829/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860627
obj = -8.147060, rho = -0.193815
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.775749
obj = -10.226711, rho = -0.137544
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.685228
obj = -12.686123, rho = -0.109545
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.603906
obj = -15.494885, rho = -0.128129
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.504781
obj = -18.824475, rho = -0.102572
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.434486
obj = -22.705775, rho = -0.112964
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.364479
obj = -27.136986, rho = -0.179682
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.302193
obj = -32.194413, rho = -0.164089
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.251579
obj = -38.410989, rho = -0.181599
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.203538
obj = -45.665812, rho = -0.216950
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.169590
obj = -54.874366, rho = -0.170658
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.139339
obj = -66.361837, rho = -0.149011
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.116542
obj = -80.998523, rho = -0.155450
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -0.785306, rho = -0.954467
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -1.120355, rho = -0.934503
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -1.592399, rho = -0.905786
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -2.250909, rho = -0.864478
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -3.155722, rho = -0.805058
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -4.369475, rho = -0.719586
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -5.933782, rho = -0.596639
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 70% (70/100) (classification)
Accuracy = 60.5% (605/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -7.808168, rho = -0.419785
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 92% (92/100) (classification)
Accuracy = 90.1% (901/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.749233
obj = -9.874018, rho = -0.328407
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.654367
obj = -12.259146, rho = -0.299591
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.577595
obj = -15.086368, rho = -0.211219
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.500000
obj = -18.367145, rho = -0.100346
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.420000
obj = -22.065843, rho = -0.030333
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.348220
obj = -26.455869, rho = -0.020549
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.297994
obj = -31.428986, rho = -0.081416
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.244438
obj = -37.184104, rho = -0.015353
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.200576
obj = -44.151646, rho = -0.027083
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.163326
obj = -52.672335, rho = -0.054495
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.138719
obj = -62.604899, rho = -0.169659
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.111849
obj = -74.139699, rho = -0.169029
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.932015, rho = -0.913818
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.323006, rho = -0.876031
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.866558, rho = -0.821677
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.609385, rho = -0.743491
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.597114, rho = -0.631025
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.850747, rho = -0.469248
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 72% (72/100) (classification)
Accuracy = 74.8% (748/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.948935
obj = -6.309642, rho = -0.294060
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.865038
obj = -7.982618, rho = -0.195362
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -9.989364, rho = -0.182070
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.674455
obj = -12.280249, rho = -0.150179
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.582406
obj = -14.981528, rho = -0.095944
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.499791
obj = -17.981883, rho = -0.152260
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.418278
obj = -21.455521, rho = -0.133291
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.341315
obj = -25.607578, rho = -0.138560
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.284484
obj = -30.368327, rho = -0.188057
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.232873
obj = -36.391846, rho = -0.176739
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.194314
obj = -43.435732, rho = -0.074733
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.159297
obj = -52.680378, rho = -0.141314
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.135916
obj = -64.317383, rho = -0.124079
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.116213
obj = -77.232712, rho = 0.004930
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -0.895175, rho = 0.882517
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -1.272013, rho = 0.831443
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.920000
obj = -1.797339, rho = 0.757405
nSV = 95, nBSV = 90
Total nSV = 95
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.920000
obj = -2.518360, rho = 0.651039
nSV = 95, nBSV = 90
Total nSV = 95
Accuracy = 54% (54/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.920000
obj = -3.483856, rho = 0.498037
nSV = 95, nBSV = 90
Total nSV = 95
Accuracy = 55% (55/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -4.724415, rho = 0.277446
nSV = 95, nBSV = 90
Total nSV = 95
Accuracy = 69% (69/100) (classification)
Accuracy = 66.2% (662/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.900000
obj = -6.207484, rho = 0.018609
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 87% (87/100) (classification)
Accuracy = 87.9% (879/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.844967
obj = -7.903943, rho = -0.164015
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.747890
obj = -9.912920, rho = -0.158118
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.680000
obj = -12.236030, rho = -0.179739
nSV = 68, nBSV = 68
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.587227
obj = -14.694347, rho = -0.192411
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.495403
obj = -17.464300, rho = -0.258278
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.415578
obj = -20.462111, rho = -0.327950
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.332113
obj = -23.612154, rho = -0.323673
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.267084
obj = -27.471684, rho = -0.284530
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.214072
obj = -32.325703, rho = -0.293406
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.177338
obj = -37.688265, rho = -0.313019
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.143337
obj = -44.232133, rho = -0.351677
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.119591
obj = -51.860620, rho = -0.354523
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.094242
obj = -60.258309, rho = -0.355086
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -0.802820, rho = 0.930785
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.143982, rho = 0.900438
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.623141, rho = 0.856785
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -2.288419, rho = 0.793992
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -3.195792, rho = 0.703668
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.6% (486/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -4.398383, rho = 0.573741
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -5.915914, rho = 0.386847
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 80% (80/100) (classification)
Accuracy = 65.2% (652/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.659456, rho = 0.118011
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 94% (94/100) (classification)
Accuracy = 91.3% (913/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.734314
obj = -9.559427, rho = 0.049249
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.641507
obj = -11.794092, rho = 0.024607
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.557373
obj = -14.423734, rho = 0.094961
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.464211
obj = -17.519298, rho = 0.156933
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.400000
obj = -21.337557, rho = 0.158090
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.335463
obj = -25.746190, rho = 0.099478
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.281308
obj = -31.270457, rho = 0.064706
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.238544
obj = -38.139295, rho = 0.054261
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.203883
obj = -46.259684, rho = 0.188353
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.179277
obj = -55.179629, rho = 0.214197
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.144118
obj = -65.027095, rho = 0.218110
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.117501
obj = -77.075122, rho = 0.278765
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.934313, rho = 0.903771
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.327763, rho = 0.861616
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.876400, rho = 0.800941
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.629750, rho = 0.713664
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -3.639253, rho = 0.588418
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.960000
obj = -4.937938, rho = 0.407961
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 68% (68/100) (classification)
Accuracy = 60.7% (607/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.960000
obj = -6.488556, rho = 0.148699
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -8.150225, rho = 0.034974
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.788684
obj = -10.030668, rho = 0.109738
nSV = 82, nBSV = 76
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.677190
obj = -12.266840, rho = 0.051999
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.572546
obj = -14.979911, rho = 0.060858
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.494798
obj = -18.188009, rho = -0.057738
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.422521
obj = -21.760736, rho = -0.084121
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.357041
obj = -25.700294, rho = -0.050534
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.289433
obj = -30.202431, rho = -0.036224
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.242342
obj = -35.148917, rho = -0.008763
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.
WARNING: using -h 0 may be faster
*
optimization finished, #iter = 126
nu = 0.198681
obj = -40.007321, rho = 0.030292
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.153730
obj = -45.391468, rho = 0.084769
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.125207
obj = -51.719619, rho = 0.126942
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*...*
optimization finished, #iter = 486
nu = 0.098976
obj = -57.307315, rho = 0.083150
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -0.913564, rho = 0.879086
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.297444, rho = 0.826071
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.831810, rho = 0.749811
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.563585, rho = 0.640116
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.539891, rho = 0.482325
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.786349, rho = 0.255351
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 75% (75/100) (classification)
Accuracy = 71.7% (717/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.921333
obj = -6.256503, rho = -0.014141
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.855294
obj = -7.939360, rho = -0.060198
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.759821
obj = -9.896493, rho = -0.003719
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.666684
obj = -12.190184, rho = -0.060138
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.575396
obj = -14.891945, rho = 0.028575
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.493130
obj = -17.937680, rho = -0.003824
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.405517
obj = -21.615174, rho = 0.026743
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.337665
obj = -26.338861, rho = 0.013798
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.290714
obj = -32.146160, rho = -0.034308
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.241246
obj = -39.151557, rho = 0.014568
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.205796
obj = -47.733596, rho = 0.023435
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.171496
obj = -58.830966, rho = 0.036031
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.148970
obj = -72.650772, rho = 0.101432
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.133454
obj = -88.319118, rho = 0.218309
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -0.839813, rho = 0.920675
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.195297, rho = 0.885895
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.860000
obj = -1.693030, rho = 0.835866
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -2.380830, rho = 0.763901
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -3.311917, rho = 0.660383
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 57% (57/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -4.530656, rho = 0.512573
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 59% (59/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.034241, rho = 0.298860
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 83% (83/100) (classification)
Accuracy = 79.3% (793/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -7.694034, rho = 0.076206
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -9.515863, rho = 0.018484
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.654846
obj = -11.454484, rho = -0.072796
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.555212
obj = -13.694244, rho = -0.057486
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.452560
obj = -16.251203, rho = -0.056916
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.377140
obj = -19.266311, rho = -0.017238
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.310595
obj = -22.915722, rho = 0.037744
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.262557
obj = -26.796362, rho = 0.173052
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.210480
obj = -31.249904, rho = 0.177554
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.175888
obj = -36.309466, rho = 0.183995
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.138189
obj = -41.601263, rho = 0.186361
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.112966
obj = -47.881052, rho = 0.194340
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.090020
obj = -54.390017, rho = 0.145561
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.957063, rho = -0.919027
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.362220, rho = -0.883524
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.929553, rho = -0.832456
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.713629, rho = -0.758996
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.775267, rho = -0.653328
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -5.165367, rho = -0.501329
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 53% (53/100) (classification)
Accuracy = 56.5% (565/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -6.881458, rho = -0.282687
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 87% (87/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.950955
obj = -8.784873, rho = -0.065048
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860335
obj = -10.803205, rho = 0.014855
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.731688
obj = -13.116420, rho = 0.058590
nSV = 75, nBSV = 69
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.606083
obj = -15.990563, rho = 0.015724
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.523752
obj = -19.424193, rho = -0.013669
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.438669
obj = -23.525221, rho = 0.015785
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.369315
obj = -28.462702, rho = 0.057169
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.308007
obj = -34.750088, rho = 0.071119
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.262978
obj = -42.339993, rho = 0.143927
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.223028
obj = -51.717494, rho = 0.141410
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.184299
obj = -63.837873, rho = 0.120446
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.164439
obj = -79.099536, rho = 0.150872
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.138380
obj = -96.961391, rho = 0.275552
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -0.746297, rho = -0.966602
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -1.064869, rho = -0.951958
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -1.513879, rho = -0.930894
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -2.140639, rho = -0.900595
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -3.002644, rho = -0.857011
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -4.160745, rho = -0.794317
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -5.657253, rho = -0.704136
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 65% (65/100) (classification)
Accuracy = 55.4% (554/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -7.459475, rho = -0.574414
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 91% (91/100) (classification)
Accuracy = 84.4% (844/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.740000
obj = -9.337683, rho = -0.419809
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.640000
obj = -11.310975, rho = -0.373427
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.540267
obj = -13.445852, rho = -0.313142
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.447519
obj = -15.911362, rho = -0.299664
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.367104
obj = -18.814940, rho = -0.362873
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.305774
obj = -22.177617, rho = -0.432537
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.245926
obj = -26.239805, rho = -0.347285
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.204836
obj = -30.958127, rho = -0.434237
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.166156
obj = -36.745818, rho = -0.494067
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.135053
obj = -44.157524, rho = -0.543029
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.115693
obj = -52.714116, rho = -0.452207
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.093031
obj = -63.364972, rho = -0.466019
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.918262, rho = -0.928223
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.307165, rho = -0.896753
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.851924, rho = -0.851484
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.605205, rho = -0.786367
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.626008, rho = -0.692700
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.7% (487/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.964536, rho = -0.557964
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 55% (55/100) (classification)
Accuracy = 54.6% (546/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.621276, rho = -0.363441
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 86% (86/100) (classification)
Accuracy = 86.4% (864/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -8.501204, rho = -0.176524
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.817589
obj = -10.609512, rho = -0.073991
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.710235
obj = -13.086585, rho = -0.057648
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.617142
obj = -15.995445, rho = 0.055634
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.528485
obj = -19.431482, rho = 0.077957
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.441062
obj = -23.381459, rho = 0.024181
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.372857
obj = -27.915422, rho = 0.057756
nSV = 42, nBSV = 33
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.304293
obj = -33.610408, rho = 0.064674
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.251974
obj = -41.013260, rho = 0.056691
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.213966
obj = -50.550651, rho = 0.042634
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.184590
obj = -62.044378, rho = 0.032485
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.157793
obj = -75.934005, rho = 0.062932
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.133375
obj = -93.398710, rho = 0.041665
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.895571, rho = -0.945816
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.272828, rho = -0.922059
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.799020, rho = -0.887885
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.521838, rho = -0.838729
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.491053, rho = -0.768019
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.739300, rho = -0.666307
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 67% (67/100) (classification)
Accuracy = 63.7% (637/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.913130
obj = -6.233836, rho = -0.532660
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 88% (88/100) (classification)
Accuracy = 87.3% (873/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.858704
obj = -7.986645, rho = -0.413197
nSV = 87, nBSV = 82
Total nSV = 87
Accuracy = 93% (93/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.757641
obj = -10.061585, rho = -0.363203
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.666228
obj = -12.562790, rho = -0.366259
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.590331
obj = -15.589429, rho = -0.373641
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.506537
obj = -19.088446, rho = -0.365260
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.434575
obj = -23.264840, rho = -0.277156
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.363009
obj = -28.305202, rho = -0.278957
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.305822
obj = -34.588221, rho = -0.287160
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.264022
obj = -42.327269, rho = -0.305364
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.222504
obj = -51.573658, rho = -0.303927
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.196005
obj = -62.410174, rho = -0.155785
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.160344
obj = -74.310220, rho = -0.218186
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.138555
obj = -87.132958, rho = -0.293901
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -0.806053, rho = -0.965295
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.150672, rho = -0.950079
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -1.636983, rho = -0.928191
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -2.317060, rho = -0.896707
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -3.255057, rho = -0.851703
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -4.521011, rho = -0.786683
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 59% (59/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -6.169648, rho = -0.693154
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 62% (62/100) (classification)
Accuracy = 53.7% (537/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -8.184468, rho = -0.558337
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 82% (82/100) (classification)
Accuracy = 82% (820/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780302
obj = -10.400046, rho = -0.420834
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 95% (95/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.699434
obj = -12.890978, rho = -0.330870
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.600000
obj = -15.881535, rho = -0.316930
nSV = 61, nBSV = 59
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.523739
obj = -19.315931, rho = -0.296329
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.444416
obj = -23.280571, rho = -0.274626
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.377937
obj = -27.811845, rho = -0.233248
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.310916
obj = -32.899420, rho = -0.245264
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.252598
obj = -39.147661, rho = -0.264396
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.211248
obj = -46.573031, rho = -0.317070
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.172202
obj = -55.786475, rho = -0.290710
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.146582
obj = -66.557781, rho = -0.251074
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.120916
obj = -79.032967, rho = -0.151815
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -0.859228, rho = -0.948427
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.222855, rho = -0.925814
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -1.731908, rho = -0.893288
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -2.435173, rho = -0.846499
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.386817, rho = -0.779197
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.631629, rho = -0.682386
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 63% (63/100) (classification)
Accuracy = 58.9% (589/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.165488, rho = -0.543128
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 86% (86/100) (classification)
Accuracy = 84% (840/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.839681
obj = -7.908850, rho = -0.409993
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.754869
obj = -9.926238, rho = -0.446799
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.680000
obj = -12.255459, rho = -0.397570
nSV = 72, nBSV = 65
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.582717
obj = -14.763414, rho = -0.364474
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.494311
obj = -17.489691, rho = -0.358965
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.413010
obj = -20.654010, rho = -0.335376
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.335715
obj = -24.130846, rho = -0.367332
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.284550
obj = -27.630869, rho = -0.441177
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.222976
obj = -31.107754, rho = -0.444693
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.173156
obj = -35.106892, rho = -0.482259
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.138885
obj = -39.596377, rho = -0.531214
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.107882
obj = -44.540135, rho = -0.546055
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*....*
optimization finished, #iter = 406
nu = 0.084151
obj = -49.737678, rho = -0.622644
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.972626, rho = 0.027614
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.381809, rho = 0.039722
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.951939, rho = 0.057138
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.733851, rho = 0.082190
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.779566, rho = 0.118227
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.120258, rho = 0.170063
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.737702, rho = 0.121850
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -8.574893, rho = 0.039904
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.834032
obj = -10.624973, rho = 0.088448
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.725644
obj = -12.907700, rho = 0.044518
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.608655
obj = -15.615156, rho = 0.011588
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.511683
obj = -18.857447, rho = 0.027782
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.429615
obj = -22.684114, rho = -0.070453
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 191
nu = 0.362118
obj = -27.201922, rho = -0.010594
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.300480
obj = -32.763514, rho = -0.036821
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.247586
obj = -39.715973, rho = -0.076550
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.202845
obj = -48.960476, rho = -0.075473
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.184554
obj = -60.435844, rho = -0.135658
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.158426
obj = -72.421779, rho = -0.127590
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.130132
obj = -86.230406, rho = -0.055138
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.859872, rho = -0.934151
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.224188, rho = -0.905279
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.734667, rho = -0.863749
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.440882, rho = -0.804010
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -3.398631, rho = -0.718597
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.656072, rho = -0.595216
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 57% (57/100) (classification)
Accuracy = 51.6% (516/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.216064, rho = -0.417739
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 82% (82/100) (classification)
Accuracy = 82.7% (827/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.971936, rho = -0.244526
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 94% (94/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.764404
obj = -9.859323, rho = -0.126272
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.677222
obj = -12.066761, rho = -0.110916
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.564448
obj = -14.649415, rho = -0.098889
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.480000
obj = -17.810467, rho = -0.056480
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.400880
obj = -21.584879, rho = -0.075405
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.342421
obj = -26.085116, rho = -0.027009
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.286482
obj = -31.707405, rho = -0.011775
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.239554
obj = -38.623386, rho = -0.043926
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.202427
obj = -47.536116, rho = -0.132990
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.172102
obj = -58.803834, rho = -0.121848
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.146882
obj = -73.106265, rho = -0.094078
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.128470
obj = -91.292136, rho = 0.019569
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -0.804583, rho = 0.941988
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -1.147629, rho = 0.916553
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.630687, rho = 0.879966
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -2.304033, rho = 0.827336
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -3.228100, rho = 0.751632
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -4.465232, rho = 0.642735
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -6.054233, rho = 0.486093
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 64% (64/100) (classification)
Accuracy = 59.8% (598/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.945658, rho = 0.260770
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 90% (90/100) (classification)
Accuracy = 90.2% (902/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -9.953788, rho = 0.072516
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.692363
obj = -12.013726, rho = -0.006077
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.569783
obj = -14.309820, rho = -0.018324
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.469573
obj = -17.130710, rho = -0.047978
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.392006
obj = -20.592785, rho = -0.069877
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.335912
obj = -24.746150, rho = -0.119713
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.278217
obj = -29.171800, rho = -0.217101
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.229477
obj = -34.192979, rho = -0.307768
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.184435
obj = -40.029893, rho = -0.320040
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.147528
obj = -47.590217, rho = -0.372671
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.122708
obj = -57.495347, rho = -0.300410
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.105427
obj = -68.219875, rho = -0.293540
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -0.881974, rho = 0.909301
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.900000
obj = -1.257314, rho = 0.868213
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.785064, rho = 0.810432
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.519062, rho = 0.727315
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -3.522852, rho = 0.607757
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 49.8% (498/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.859101, rho = 0.435778
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 58% (58/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -6.558476, rho = 0.188395
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 85% (85/100) (classification)
Accuracy = 78.3% (783/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.876142
obj = -8.554577, rho = -0.088574
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -10.940429, rho = -0.143170
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.723779
obj = -13.740211, rho = -0.143754
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 94% (94/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.640000
obj = -16.953223, rho = -0.189033
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 94% (94/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.546379
obj = -20.761703, rho = -0.137150
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 94% (94/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.474577
obj = -25.270318, rho = -0.011628
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 94% (94/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.398310
obj = -30.607824, rho = -0.051264
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.335786
obj = -37.122093, rho = -0.070833
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.282631
obj = -45.048933, rho = -0.097932
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.239458
obj = -54.724152, rho = -0.086771
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.198737
obj = -66.648422, rho = -0.139992
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.168970
obj = -81.812843, rho = -0.152849
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.147522
obj = -99.931256, rho = -0.133620
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.932523, rho = 0.842109
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.324059, rho = 0.772881
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.868736, rho = 0.673301
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.613890, rho = 0.530059
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 49.5% (495/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.606437, rho = 0.324014
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 54% (54/100) (classification)
Accuracy = 51.9% (519/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.870037, rho = 0.027628
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 79% (79/100) (classification)
Accuracy = 80.6% (806/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.368000, rho = -0.283373
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 91% (91/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.855372
obj = -8.134304, rho = -0.300055
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.764052
obj = -10.295730, rho = -0.236427
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.669956
obj = -12.977387, rho = -0.234334
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.600000
obj = -16.279758, rho = -0.252067
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.518063
obj = -20.287773, rho = -0.164468
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.455517
obj = -25.175377, rho = -0.095642
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.383746
obj = -31.196618, rho = -0.162137
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.337464
obj = -38.545456, rho = -0.083777
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.283855
obj = -47.919901, rho = -0.090587
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.249703
obj = -59.860123, rho = 0.008498
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.211552
obj = -74.996209, rho = 0.015610
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.197015
obj = -92.656686, rho = -0.093597
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.168259
obj = -110.879875, rho = -0.234737
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -0.856527, rho = 0.898627
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.217268, rho = 0.854181
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -1.720348, rho = 0.790246
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -2.411253, rho = 0.698279
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -3.337325, rho = 0.565990
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -4.529222, rho = 0.375698
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 65% (65/100) (classification)
Accuracy = 59% (590/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -5.953595, rho = 0.101166
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 92% (92/100) (classification)
Accuracy = 87% (870/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -7.521566, rho = -0.001905
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.732543
obj = -9.304650, rho = -0.090955
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.644354
obj = -11.295478, rho = -0.136908
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.546231
obj = -13.382486, rho = -0.089051
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.454767
obj = -15.702550, rho = -0.130923
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.368860
obj = -18.240236, rho = -0.068130
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.293723
obj = -21.303911, rho = -0.057677
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.237152
obj = -25.168031, rho = -0.041823
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.192916
obj = -30.075230, rho = -0.060347
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.162168
obj = -36.010308, rho = 0.066759
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.134995
obj = -42.662740, rho = 0.013592
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.114885
obj = -50.161514, rho = -0.066593
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.100582
obj = -55.741850, rho = -0.200883
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.961128, rho = -0.032677
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.358019, rho = -0.047004
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.902715, rho = -0.067613
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.631998, rho = -0.097257
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.568818, rho = -0.139900
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.689288, rho = -0.177325
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 99% (99/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.915374
obj = -5.979944, rho = -0.222844
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.821066
obj = -7.501421, rho = -0.253948
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.713052
obj = -9.313047, rho = -0.235409
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.634007
obj = -11.421163, rho = -0.264939
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.540000
obj = -13.840841, rho = -0.281301
nSV = 55, nBSV = 53
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.456754
obj = -16.783478, rho = -0.286573
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.389065
obj = -20.118849, rho = -0.338134
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.325225
obj = -23.704425, rho = -0.304424
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.273573
obj = -27.753541, rho = -0.314453
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.215996
obj = -32.163546, rho = -0.293567
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.174303
obj = -37.772875, rho = -0.255344
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.141649
obj = -44.830157, rho = -0.343204
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 232
nu = 0.124613
obj = -52.140774, rho = -0.620387
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.096474
obj = -59.321324, rho = -0.639069
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -0.899107, rho = 0.913211
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -1.280144, rho = 0.875158
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -1.814159, rho = 0.820421
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -2.553165, rho = 0.740882
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.920000
obj = -3.555874, rho = 0.627865
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.920000
obj = -4.873423, rho = 0.464702
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 58% (58/100) (classification)
Accuracy = 54.4% (544/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.920000
obj = -6.510430, rho = 0.230001
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 82% (82/100) (classification)
Accuracy = 83.2% (832/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.882976
obj = -8.363915, rho = 0.068008
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.808361
obj = -10.479098, rho = -0.028329
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.707219
obj = -12.914852, rho = -0.058604
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.603017
obj = -15.755437, rho = -0.002352
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.520000
obj = -19.146592, rho = -0.085749
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.443160
obj = -23.030593, rho = -0.171586
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.373161
obj = -27.334520, rho = -0.161548
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.302343
obj = -32.372950, rho = -0.130067
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.248893
obj = -38.718511, rho = -0.065581
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.210771
obj = -45.915281, rho = -0.184420
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.169065
obj = -54.788971, rho = -0.176910
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.137569
obj = -66.680480, rho = -0.227342
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.119996
obj = -81.895169, rho = -0.376450
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -0.873443, rho = -0.927146
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.239655, rho = -0.895203
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -1.748526, rho = -0.849254
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -2.443458, rho = -0.783160
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.3% (503/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -3.366418, rho = -0.688086
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 55% (55/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -4.535416, rho = -0.551327
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 74% (74/100) (classification)
Accuracy = 65.5% (655/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -5.888729, rho = -0.354607
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 93% (93/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.819767
obj = -7.349218, rho = -0.262103
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.727083
obj = -8.986199, rho = -0.212355
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.615378
obj = -10.829918, rho = -0.162896
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.515572
obj = -12.975714, rho = -0.153338
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.435828
obj = -15.505777, rho = -0.159831
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.357453
obj = -18.388370, rho = -0.159213
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.289545
obj = -21.950254, rho = -0.190758
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.251040
obj = -26.095863, rho = -0.096170
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.207579
obj = -30.319771, rho = -0.021651
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*....*
optimization finished, #iter = 484
nu = 0.169199
obj = -35.114580, rho = 0.022213
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.137416
obj = -40.292544, rho = -0.025858
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.110183
obj = -45.697092, rho = -0.080425
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.089709
obj = -49.273264, rho = -0.246434
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -0.911249, rho = 0.876885
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.292653, rho = 0.822905
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -1.821897, rho = 0.745258
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -2.543074, rho = 0.633567
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -3.497451, rho = 0.472904
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 54% (54/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -4.698535, rho = 0.241799
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 77% (77/100) (classification)
Accuracy = 73% (730/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -6.077151, rho = -0.013192
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.838930
obj = -7.645261, rho = -0.078153
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.749967
obj = -9.420568, rho = -0.091747
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.631014
obj = -11.472599, rho = -0.099001
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.539036
obj = -13.991578, rho = -0.026371
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.458428
obj = -16.958107, rho = -0.026085
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.381805
obj = -20.551347, rho = -0.078828
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.333665
obj = -24.699389, rho = 0.006383
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.271358
obj = -29.442997, rho = 0.038222
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.226016
obj = -35.523756, rho = 0.094522
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.185774
obj = -42.981862, rho = 0.135193
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.155707
obj = -52.314434, rho = 0.076133
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.128438
obj = -64.893558, rho = 0.080661
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.111785
obj = -81.978891, rho = 0.190103
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -0.859548, rho = -0.938626
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.223519, rho = -0.911717
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -1.733281, rho = -0.873009
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -2.438015, rho = -0.817330
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -3.392699, rho = -0.737239
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -4.643799, rho = -0.622032
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 58% (58/100) (classification)
Accuracy = 53.1% (531/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.190668, rho = -0.456311
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 84% (84/100) (classification)
Accuracy = 79.4% (794/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.845998
obj = -7.930147, rho = -0.292705
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 95% (95/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.769677
obj = -9.846158, rho = -0.178027
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.666442
obj = -12.000917, rho = -0.204896
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.560255
obj = -14.599245, rho = -0.183908
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.482335
obj = -17.714669, rho = -0.158613
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.404364
obj = -21.420558, rho = -0.149607
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.344719
obj = -25.794220, rho = -0.187999
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 198
nu = 0.286397
obj = -30.681353, rho = -0.218713
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.238432
obj = -36.500941, rho = -0.254563
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.200912
obj = -43.138210, rho = -0.362069
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.171443
obj = -49.590109, rho = -0.385462
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.130096
obj = -56.599473, rho = -0.377247
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.103631
obj = -65.975796, rho = -0.376144
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -0.934479, rho = 0.889876
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.328106, rho = 0.841593
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -1.877110, rho = 0.772139
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -2.631219, rho = 0.672233
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.960000
obj = -3.642291, rho = 0.528524
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 52% (52/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -4.944225, rho = 0.321806
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 64% (64/100) (classification)
Accuracy = 66.6% (666/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -6.501565, rho = 0.024451
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 94% (94/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.892765
obj = -8.246363, rho = -0.108754
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.805111
obj = -10.228951, rho = -0.185123
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.693741
obj = -12.429732, rho = -0.214400
nSV = 73, nBSV = 67
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.586686
obj = -15.053268, rho = -0.235325
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.495502
obj = -18.153145, rho = -0.210747
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.423330
obj = -21.663305, rho = -0.213208
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.347994
obj = -25.793649, rho = -0.218766
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.287074
obj = -30.796773, rho = -0.269826
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.236766
obj = -36.773826, rho = -0.232081
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.193546
obj = -44.275253, rho = -0.212890
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.162532
obj = -53.876281, rho = -0.239244
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.137661
obj = -65.549679, rho = -0.245986
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 196
nu = 0.119957
obj = -78.884738, rho = -0.079635
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.961751, rho = -0.058229
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 90% (90/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.359307, rho = -0.083759
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 90% (90/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.905380, rho = -0.120484
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 90% (90/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.637514, rho = -0.173310
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 90% (90/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.580230, rho = -0.249297
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 90% (90/100) (classification)
Accuracy = 84.3% (843/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.720093, rho = -0.350736
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 91% (91/100) (classification)
Accuracy = 84.9% (849/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.909433
obj = -6.060882, rho = -0.349680
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 91% (91/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.822350
obj = -7.694501, rho = -0.354918
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.718341
obj = -9.712647, rho = -0.341691
nSV = 76, nBSV = 70
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.639235
obj = -12.242852, rho = -0.269208
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.561370
obj = -15.342363, rho = -0.292559
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.497490
obj = -18.893284, rho = -0.289612
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.426458
obj = -23.110152, rho = -0.311084
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.357838
obj = -28.275776, rho = -0.277418
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.312571
obj = -34.601836, rho = -0.277845
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.263313
obj = -42.283047, rho = -0.326494
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.228725
obj = -50.918191, rho = -0.449561
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.189606
obj = -60.909335, rho = -0.325540
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.158835
obj = -72.524602, rho = -0.358081
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*..*
optimization finished, #iter = 489
nu = 0.131180
obj = -86.027777, rho = -0.400312
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.944366, rho = 0.800460
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.335949, rho = 0.712971
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.875194, rho = 0.587123
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 46.2% (462/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.601153, rho = 0.406098
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 52% (52/100) (classification)
Accuracy = 46.4% (464/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.542539, rho = 0.145701
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 76% (76/100) (classification)
Accuracy = 70.7% (707/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.964061
obj = -4.686321, rho = -0.144988
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 97% (97/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.897091
obj = -6.059529, rho = -0.221313
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.841264
obj = -7.686437, rho = -0.261879
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.739515
obj = -9.463612, rho = -0.284002
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.647743
obj = -11.493704, rho = -0.239079
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.539550
obj = -13.847493, rho = -0.267713
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.459645
obj = -16.708551, rho = -0.209437
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.395287
obj = -19.782578, rho = -0.229564
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.320281
obj = -23.132718, rho = -0.309002
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.261997
obj = -26.927402, rho = -0.351409
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.211636
obj = -31.250334, rho = -0.304017
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.174279
obj = -36.285143, rho = -0.297019
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.139343
obj = -41.791506, rho = -0.243191
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.111409
obj = -47.971833, rho = -0.188178
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.087741
obj = -55.276416, rho = -0.155208
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -0.931316, rho = -0.905065
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.321562, rho = -0.863441
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -1.863569, rho = -0.803566
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.603199, rho = -0.717440
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.584315, rho = -0.593551
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 51.7% (517/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.824265, rho = -0.415343
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 80% (80/100) (classification)
Accuracy = 77.2% (772/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.259741, rho = -0.203748
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.857565
obj = -7.898595, rho = -0.070757
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.765466
obj = -9.778484, rho = -0.069197
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.683731
obj = -11.778277, rho = 0.002411
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.568651
obj = -13.899126, rho = -0.001169
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.465029
obj = -16.301176, rho = 0.048140
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.379963
obj = -19.190950, rho = 0.101662
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.315141
obj = -22.611823, rho = 0.089232
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.250457
obj = -26.642629, rho = 0.077423
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.210319
obj = -31.350957, rho = 0.075914
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.170017
obj = -36.741564, rho = 0.135790
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.135036
obj = -43.715313, rho = 0.130770
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.118177
obj = -52.296138, rho = -0.050952
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.093846
obj = -61.216590, rho = -0.073741
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -0.840536, rho = 0.922191
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -1.196793, rho = 0.888075
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -1.696126, rho = 0.839002
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -2.387236, rho = 0.768412
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -3.325172, rho = 0.666872
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -4.558080, rho = 0.520812
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 57% (57/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.090987, rho = 0.310712
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 86% (86/100) (classification)
Accuracy = 76% (760/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.798223, rho = 0.008494
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760985
obj = -9.492924, rho = -0.052777
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.645117
obj = -11.473303, rho = 0.004663
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.548750
obj = -13.709431, rho = -0.035401
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.454119
obj = -16.369626, rho = -0.060252
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.382495
obj = -19.461675, rho = -0.031032
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.319125
obj = -23.007151, rho = 0.006947
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.262415
obj = -26.909811, rho = 0.112940
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.213356
obj = -31.441687, rho = 0.082490
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.172902
obj = -36.289264, rho = 0.004166
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.143317
obj = -41.605560, rho = 0.068795
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.117923
obj = -46.482169, rho = 0.150650
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.090561
obj = -50.244638, rho = 0.275396
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -0.802181, rho = 0.918216
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.142658, rho = 0.882358
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -1.620403, rho = 0.830778
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -2.282753, rho = 0.756583
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -3.184069, rho = 0.649856
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 59% (59/100) (classification)
Accuracy = 50% (500/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -4.374125, rho = 0.496336
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 60% (60/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -5.865722, rho = 0.275505
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 82% (82/100) (classification)
Accuracy = 78.1% (781/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.809173
obj = -7.557695, rho = -0.004807
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.734917
obj = -9.403720, rho = -0.040647
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.653974
obj = -11.405075, rho = -0.053264
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.540681
obj = -13.652647, rho = -0.055653
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.453063
obj = -16.260543, rho = -0.046189
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.374459
obj = -19.296018, rho = -0.046292
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.305044
obj = -23.120962, rho = -0.044140
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.257231
obj = -27.650833, rho = -0.117182
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.220253
obj = -32.767967, rho = -0.257183
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.177961
obj = -38.362578, rho = -0.326125
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 168
nu = 0.145672
obj = -45.331301, rho = -0.353490
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.120532
obj = -52.628405, rho = -0.418082
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.098962
obj = -60.665447, rho = -0.397548
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.899747, rho = -0.938978
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.281468, rho = -0.912223
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.816898, rho = -0.873738
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.558831, rho = -0.818378
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.567596, rho = -0.738746
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 50.5% (505/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.897679, rho = -0.624199
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 58% (58/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.560618, rho = -0.459429
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 80% (80/100) (classification)
Accuracy = 81.7% (817/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.496990, rho = -0.313164
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 93% (93/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.814061
obj = -10.710156, rho = -0.307758
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 95% (95/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.705664
obj = -13.374314, rho = -0.286832
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.617541
obj = -16.667455, rho = -0.209578
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.542818
obj = -20.724664, rho = -0.175906
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.463057
obj = -25.516485, rho = -0.063394
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.399306
obj = -31.392568, rho = -0.123289
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.338499
obj = -38.567351, rho = -0.086894
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.289813
obj = -47.666698, rho = -0.095149
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.242163
obj = -59.268940, rho = -0.071547
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.213522
obj = -74.697050, rho = 0.054542
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.183837
obj = -94.193540, rho = 0.113962
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 299
nu = 0.159522
obj = -119.525591, rho = 0.096278
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.966897, rho = -0.036152
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.369956, rho = -0.052003
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.927414, rho = -0.074804
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.683105, rho = -0.107602
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.674565, rho = -0.154780
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -4.902997, rho = -0.222643
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.321063, rho = -0.190897
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.869512
obj = -7.954040, rho = -0.189336
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.767188
obj = -9.822039, rho = -0.183967
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.656625
obj = -12.021310, rho = -0.150896
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.560000
obj = -14.777028, rho = -0.119739
nSV = 57, nBSV = 55
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.474710
obj = -18.069406, rho = -0.116772
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.410718
obj = -22.031886, rho = -0.137244
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.356346
obj = -26.709143, rho = -0.225390
nSV = 37, nBSV = 34
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.303657
obj = -31.809131, rho = -0.290732
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.248904
obj = -37.193176, rho = -0.321384
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.200784
obj = -43.608113, rho = -0.374183
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.163529
obj = -51.872759, rho = -0.430611
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.134733
obj = -61.476689, rho = -0.562740
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.113153
obj = -73.077632, rho = -0.743820
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -0.743250, rho = 0.937003
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760000
obj = -1.058564, rho = 0.908906
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -1.500834, rho = 0.869100
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -2.113647, rho = 0.811707
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -2.946793, rho = 0.729150
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 62% (62/100) (classification)
Accuracy = 52% (520/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -4.045181, rho = 0.609986
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 62% (62/100) (classification)
Accuracy = 52.1% (521/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -5.418135, rho = 0.438984
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 80% (80/100) (classification)
Accuracy = 67.5% (675/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.760000
obj = -6.964705, rho = 0.193026
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 92.2% (922/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.691510
obj = -8.470835, rho = 0.104400
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.583484
obj = -10.123090, rho = 0.046627
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.485964
obj = -11.995479, rho = 0.002097
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.400721
obj = -14.163157, rho = 0.039586
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.330955
obj = -16.585052, rho = 0.001364
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.274973
obj = -19.341393, rho = -0.011102
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.218536
obj = -22.346213, rho = 0.048733
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.175716
obj = -26.033456, rho = 0.086086
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.141641
obj = -30.456826, rho = 0.052691
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.115319
obj = -35.590078, rho = 0.032449
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.095041
obj = -41.521829, rho = 0.057872
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.075909
obj = -48.517332, rho = -0.010410
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.972712, rho = -0.025169
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 91% (91/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.381986, rho = -0.036204
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 91% (91/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.952307, rho = -0.052078
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 91% (91/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.734612, rho = -0.074912
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 91% (91/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.781140, rho = -0.107757
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 91% (91/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.123514, rho = -0.155003
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 91% (91/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.739587, rho = -0.177292
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 92% (92/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -8.661199, rho = -0.129026
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 93% (93/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.827483
obj = -10.865569, rho = -0.210437
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.728059
obj = -13.449410, rho = -0.183351
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.625544
obj = -16.505092, rho = -0.128215
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.539315
obj = -20.168453, rho = -0.119831
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.459548
obj = -24.671557, rho = -0.141946
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.394829
obj = -30.053991, rho = -0.106968
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.328145
obj = -36.266761, rho = -0.069743
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*..*.*
optimization finished, #iter = 273
nu = 0.273898
obj = -44.094249, rho = -0.032910
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.229945
obj = -54.259211, rho = -0.047154
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.198234
obj = -66.059934, rho = 0.012808
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.170465
obj = -80.401955, rho = 0.034942
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.140087
obj = -97.900147, rho = 0.076811
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.924431, rho = 0.847825
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.307315, rho = 0.781104
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.834090, rho = 0.685129
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -2.542204, rho = 0.547074
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 53.4% (534/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -3.458107, rho = 0.348489
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 62% (62/100) (classification)
Accuracy = 60.9% (609/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.563122, rho = 0.062834
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 87% (87/100) (classification)
Accuracy = 83.9% (839/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.895512
obj = -5.783162, rho = -0.047602
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.804684
obj = -7.208783, rho = -0.145477
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.701711
obj = -8.869381, rho = -0.109884
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.590984
obj = -10.879331, rho = -0.088685
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.518479
obj = -13.285952, rho = 0.011253
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.444404
obj = -15.876443, rho = 0.001696
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.375165
obj = -18.633324, rho = -0.049528
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.306883
obj = -21.677817, rho = -0.029514
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.249476
obj = -24.885310, rho = 0.055406
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.206148
obj = -28.180231, rho = 0.185108
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 184
nu = 0.159207
obj = -31.110370, rho = 0.189872
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.124603
obj = -34.171735, rho = 0.195035
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.096610
obj = -36.817549, rho = 0.147217
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*.....*
optimization finished, #iter = 875
nu = 0.071330
obj = -39.118802, rho = 0.141420
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -0.954506, rho = 0.884891
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.356929, rho = 0.834421
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -1.918605, rho = 0.761823
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.980000
obj = -2.690978, rho = 0.657395
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49% (490/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.728398, rho = 0.507180
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -5.068389, rho = 0.291103
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 74% (74/100) (classification)
Accuracy = 71.4% (714/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -6.680796, rho = -0.019713
nSV = 99, nBSV = 97
Total nSV = 99
Accuracy = 95% (95/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -8.479591, rho = 0.024270
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -10.511367, rho = -0.024640
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.695805
obj = -12.958207, rho = -0.017880
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.611515
obj = -15.894686, rho = 0.038963
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.509935
obj = -19.499864, rho = 0.057296
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.448727
obj = -23.855187, rho = 0.039497
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.385686
obj = -28.471489, rho = 0.098152
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.314721
obj = -33.737806, rho = 0.075834
nSV = 37, nBSV = 27
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.256983
obj = -40.479534, rho = 0.013041
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.215376
obj = -48.827492, rho = -0.030777
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.179612
obj = -59.122793, rho = -0.086566
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.153341
obj = -71.055768, rho = -0.079924
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.129771
obj = -85.405334, rho = 0.051936
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.913602, rho = -0.917052
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.297523, rho = -0.880683
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.831973, rho = -0.828369
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.563923, rho = -0.753117
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.3% (473/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.540590, rho = -0.644872
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.787795, rho = -0.489166
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 77% (77/100) (classification)
Accuracy = 72.9% (729/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.933845
obj = -6.256278, rho = -0.286255
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.842387
obj = -7.963794, rho = -0.290421
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 94% (94/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.755234
obj = -10.060684, rho = -0.225944
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 95% (95/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.675937
obj = -12.507057, rho = -0.125545
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.590384
obj = -15.270471, rho = -0.102671
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.503635
obj = -18.603851, rho = -0.125489
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.427376
obj = -22.423358, rho = -0.127360
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.354605
obj = -26.961091, rho = -0.153518
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.295237
obj = -32.467896, rho = -0.214210
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.242025
obj = -39.612838, rho = -0.245794
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.205175
obj = -48.981376, rho = -0.202385
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 112
nu = 0.177541
obj = -60.462260, rho = -0.349000
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.151830
obj = -75.028824, rho = -0.336975
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.128872
obj = -93.985224, rho = -0.418632
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -0.955663, rho = -0.904832
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.359324, rho = -0.863105
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -1.923559, rho = -0.803084
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -2.701229, rho = -0.716746
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 51.2% (512/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -3.749608, rho = -0.592553
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 52.3% (523/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -5.112276, rho = -0.413908
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 73% (73/100) (classification)
Accuracy = 79.7% (797/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.980000
obj = -6.771604, rho = -0.156936
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 94% (94/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.914021
obj = -8.686851, rho = -0.065903
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 95% (95/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.822330
obj = -10.926778, rho = 0.045656
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.720000
obj = -13.707789, rho = 0.052001
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.632409
obj = -17.092548, rho = 0.053511
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.552946
obj = -21.173640, rho = 0.061898
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.474192
obj = -26.181611, rho = 0.091584
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.402897
obj = -32.318981, rho = -0.033441
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.344348
obj = -40.084166, rho = -0.097509
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.303645
obj = -50.083849, rho = -0.112277
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.264264
obj = -61.606131, rho = 0.005078
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.223623
obj = -75.769593, rho = 0.029415
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 288
nu = 0.187304
obj = -93.688732, rho = 0.009447
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.164799
obj = -117.534602, rho = 0.193582
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -0.969722, rho = -0.029407
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.375800, rho = -0.042301
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -1.939507, rho = -0.060848
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -2.708127, rho = -0.087527
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -3.726339, rho = -0.125902
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 50
nu = 1.000000
obj = -5.010125, rho = -0.181104
nSV = 100, nBSV = 100
Total nSV = 100
Accuracy = 94% (94/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.510399, rho = -0.163767
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.903273
obj = -8.150232, rho = -0.089751
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.793139
obj = -9.987883, rho = -0.128697
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.674660
obj = -12.201130, rho = -0.122170
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.580487
obj = -14.829659, rho = -0.105678
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.488251
obj = -17.896439, rho = -0.180799
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.414249
obj = -21.265155, rho = -0.249366
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.343513
obj = -25.284993, rho = -0.282927
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.283394
obj = -29.866398, rho = -0.284699
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.238230
obj = -35.215208, rho = -0.256440
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.194575
obj = -40.640598, rho = -0.215435
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.155982
obj = -46.683855, rho = -0.268200
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.133416
obj = -52.567545, rho = -0.190136
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.100112
obj = -57.344541, rho = -0.160318
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.950057, rho = 0.854549
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.347724, rho = 0.790775
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.899559, rho = 0.699041
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.651568, rho = 0.567085
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.646854, rho = 0.377274
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 58% (58/100) (classification)
Accuracy = 54.2% (542/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.899663, rho = 0.104240
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 83% (83/100) (classification)
Accuracy = 84.7% (847/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.377380, rho = -0.071337
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.865394
obj = -8.147792, rho = -0.048175
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.774609
obj = -10.242057, rho = 0.006692
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.696867
obj = -12.620374, rho = 0.071773
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.596577
obj = -15.374618, rho = 0.041702
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.511156
obj = -18.490495, rho = 0.061848
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.437828
obj = -21.873450, rho = -0.012438
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 216
nu = 0.357045
obj = -25.405068, rho = 0.053999
nSV = 42, nBSV = 32
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.286094
obj = -29.646538, rho = -0.017533
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.228352
obj = -34.908235, rho = 0.013804
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.185109
obj = -41.724548, rho = 0.011145
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 286
nu = 0.151057
obj = -50.715885, rho = 0.010486
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.132907
obj = -61.542196, rho = 0.032814
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.113482
obj = -73.306413, rho = -0.089828
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.740000
obj = -0.727351, rho = 0.954007
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.740000
obj = -1.038281, rho = 0.933841
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.740000
obj = -1.477008, rho = 0.904834
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.740000
obj = -2.090447, rho = 0.863108
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.740000
obj = -2.936333, rho = 0.803088
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -4.077542, rho = 0.716752
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.9% (509/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -5.562779, rho = 0.592214
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 64% (64/100) (classification)
Accuracy = 52.5% (525/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -7.375736, rho = 0.413420
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 87% (87/100) (classification)
Accuracy = 77.4% (774/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.720000
obj = -9.336442, rho = 0.218823
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.649456
obj = -11.429789, rho = 0.127967
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.549886
obj = -13.724038, rho = 0.119157
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.457793
obj = -16.346785, rho = 0.106838
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.374440
obj = -19.456330, rho = 0.131459
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.313381
obj = -23.239056, rho = 0.103062
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.255031
obj = -27.728655, rho = 0.136471
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.218180
obj = -33.204081, rho = -0.014826
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.177319
obj = -39.564571, rho = -0.033507
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.144854
obj = -47.548516, rho = -0.021549
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.121915
obj = -57.674588, rho = 0.038075
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.105009
obj = -69.166920, rho = 0.172991
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -0.908699, rho = -0.925305
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.287376, rho = -0.892555
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -1.810979, rho = -0.845445
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -2.520483, rho = -0.777681
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 50.6% (506/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -3.450706, rho = -0.680205
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 66% (66/100) (classification)
Accuracy = 56% (560/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -4.601812, rho = -0.539991
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 89% (89/100) (classification)
Accuracy = 79.9% (799/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.893194
obj = -5.907426, rho = -0.453509
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 94% (94/100) (classification)
Accuracy = 90.2% (902/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.796505
obj = -7.522837, rho = -0.486609
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 94% (94/100) (classification)
Accuracy = 91.7% (917/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.700000
obj = -9.535217, rho = -0.485286
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 94% (94/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.620000
obj = -12.091433, rho = -0.450382
nSV = 63, nBSV = 61
Total nSV = 63
Accuracy = 95% (95/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.554560
obj = -15.208152, rho = -0.399719
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 95% (95/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.486877
obj = -18.958221, rho = -0.338261
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 94% (94/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.421066
obj = -23.612458, rho = -0.320994
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.360000
obj = -29.590048, rho = -0.355565
nSV = 37, nBSV = 35
Total nSV = 37
Accuracy = 94% (94/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.314995
obj = -36.984584, rho = -0.464847
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 94% (94/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.273556
obj = -46.436502, rho = -0.470742
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.235520
obj = -58.392769, rho = -0.497108
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.204844
obj = -74.403826, rho = -0.469419
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.177934
obj = -95.013178, rho = -0.529870
nSV = 26, nBSV = 14
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.156581
obj = -123.039051, rho = -0.631558
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -0.863048, rho = 0.916695
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.230759, rho = 0.880170
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 56% (56/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.748266, rho = 0.827680
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -2.469020, rho = 0.752127
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -3.456852, rho = 0.643447
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 56% (56/100) (classification)
Accuracy = 48.8% (488/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -4.776542, rho = 0.486410
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 57% (57/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -6.465333, rho = 0.260745
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 75% (75/100) (classification)
Accuracy = 73.7% (737/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.863656
obj = -8.468030, rho = 0.016494
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 92% (92/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -10.728143, rho = -0.012006
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.713154
obj = -13.280241, rho = -0.032854
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.632103
obj = -16.256561, rho = 0.046091
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.525691
obj = -19.745600, rho = 0.034961
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.442806
obj = -24.050795, rho = 0.058901
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.378799
obj = -29.528863, rho = 0.068718
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.326039
obj = -35.641763, rho = -0.085735
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.271088
obj = -42.997663, rho = -0.089553
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.234820
obj = -51.419298, rho = -0.072688
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.196255
obj = -60.603635, rho = -0.090934
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.160225
obj = -70.222893, rho = -0.201670
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 245
nu = 0.130764
obj = -81.638970, rho = -0.158559
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -0.730153, rho = -0.964494
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -1.044078, rho = -0.948927
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -1.489004, rho = -0.926534
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -2.115271, rho = -0.895138
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 63% (63/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.740000
obj = -2.987708, rho = -0.849188
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 63% (63/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.740000
obj = -4.183842, rho = -0.783065
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 63% (63/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.740000
obj = -5.782728, rho = -0.687305
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 63% (63/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.740000
obj = -7.830840, rho = -0.550205
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 74% (74/100) (classification)
Accuracy = 71.1% (711/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.740000
obj = -10.255938, rho = -0.354099
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 96% (96/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.684722
obj = -12.896565, rho = -0.233465
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.612319
obj = -15.903593, rho = -0.218980
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.525776
obj = -19.086917, rho = -0.171193
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.436176
obj = -22.808597, rho = -0.172501
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.357303
obj = -27.424617, rho = -0.149829
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.310951
obj = -32.732106, rho = -0.092814
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.254087
obj = -38.509843, rho = -0.200316
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.210332
obj = -45.298267, rho = -0.260235
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.173449
obj = -52.418242, rho = -0.171873
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.141102
obj = -60.180191, rho = -0.167440
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.110818
obj = -69.679989, rho = -0.195214
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -0.880912, rho = 0.925332
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -1.255113, rho = 0.892890
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -1.780510, rho = 0.845928
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -2.509639, rho = 0.778376
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -3.503355, rho = 0.681204
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 55% (55/100) (classification)
Accuracy = 47.8% (478/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -4.818758, rho = 0.541222
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 55% (55/100) (classification)
Accuracy = 48.9% (489/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -6.475004, rho = 0.339699
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 78% (78/100) (classification)
Accuracy = 78.4% (784/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.899115
obj = -8.369349, rho = 0.053482
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.798818
obj = -10.498174, rho = 0.061618
nSV = 83, nBSV = 77
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.720000
obj = -12.946215, rho = -0.029701
nSV = 72, nBSV = 72
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.612840
obj = -15.670022, rho = -0.011139
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.523567
obj = -18.879018, rho = -0.096782
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.435886
obj = -22.435062, rho = -0.069881
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.362030
obj = -26.788347, rho = -0.101710
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.298808
obj = -31.669800, rho = -0.077539
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.247703
obj = -37.533418, rho = -0.114601
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.209272
obj = -43.630255, rho = -0.058529
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.164865
obj = -50.532063, rho = -0.061456
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.134569
obj = -58.680877, rho = -0.085968
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.111136
obj = -67.251372, rho = -0.101022
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.948833, rho = 0.868547
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.345192, rho = 0.810911
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.894319, rho = 0.728005
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.640726, rho = 0.608749
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.3% (493/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.624420, rho = 0.437205
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 55% (55/100) (classification)
Accuracy = 54% (540/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -4.853243, rho = 0.190448
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 89% (89/100) (classification)
Accuracy = 89.3% (893/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -6.243627, rho = -0.061350
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.797759, rho = -0.044908
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.751033
obj = -9.630285, rho = -0.078509
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.660000
obj = -11.768170, rho = -0.054643
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.560000
obj = -14.180120, rho = 0.008397
nSV = 57, nBSV = 55
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.467604
obj = -16.954319, rho = -0.012176
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.391640
obj = -20.372185, rho = -0.080553
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.326415
obj = -24.331157, rho = -0.012889
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 192
nu = 0.276746
obj = -28.733212, rho = 0.024129
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.233985
obj = -33.391576, rho = 0.048348
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.186614
obj = -38.183206, rho = 0.113185
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.147529
obj = -43.222921, rho = 0.081209
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.114543
obj = -49.402868, rho = 0.067685
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.099237
obj = -56.146452, rho = 0.255383
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -0.932208, rho = 0.862047
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.323407, rho = 0.801562
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -1.867387, rho = 0.714556
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -2.611100, rho = 0.589404
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 52% (52/100) (classification)
Accuracy = 50.4% (504/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -3.600663, rho = 0.409378
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 54% (54/100) (classification)
Accuracy = 51.5% (515/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -4.858091, rho = 0.150419
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 81% (81/100) (classification)
Accuracy = 78.6% (786/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.945462
obj = -6.326212, rho = -0.159915
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860980
obj = -8.021736, rho = -0.127672
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.770725
obj = -9.984848, rho = -0.123156
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.666708
obj = -12.337893, rho = -0.109368
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.576536
obj = -15.131440, rho = -0.111314
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.495957
obj = -18.476485, rho = -0.049718
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.428896
obj = -22.370278, rho = -0.046003
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.362051
obj = -26.679754, rho = -0.102279
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.300591
obj = -31.765105, rho = -0.156893
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.247075
obj = -37.346320, rho = -0.206418
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.199064
obj = -44.218104, rho = -0.238550
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.176376
obj = -52.396835, rho = -0.157913
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 181
nu = 0.139714
obj = -60.009273, rho = -0.217860
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.112110
obj = -68.568321, rho = -0.209912
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -0.918346, rho = -0.939172
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.307338, rho = -0.912502
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -1.852283, rho = -0.874138
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -2.605947, rho = -0.818954
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -3.627544, rho = -0.739574
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 53% (53/100) (classification)
Accuracy = 48.2% (482/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.967715, rho = -0.625390
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 62% (62/100) (classification)
Accuracy = 58.1% (581/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.627851, rho = -0.461143
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 89% (89/100) (classification)
Accuracy = 85.1% (851/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.900197
obj = -8.507180, rho = -0.303416
nSV = 93, nBSV = 89
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.830947
obj = -10.669579, rho = -0.373770
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.713333
obj = -13.060926, rho = -0.395197
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.627238
obj = -15.882976, rho = -0.310764
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.531988
obj = -19.089689, rho = -0.264512
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.441807
obj = -22.687119, rho = -0.226648
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.370386
obj = -26.828464, rho = -0.196944
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.297023
obj = -31.845857, rho = -0.196928
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.249868
obj = -37.640063, rho = -0.323592
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.204144
obj = -44.301976, rho = -0.347863
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.162642
obj = -52.565008, rho = -0.342370
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 288
nu = 0.137562
obj = -62.906095, rho = -0.330724
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.111529
obj = -75.548792, rho = -0.355798
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.940000
obj = -0.912498, rho = 0.876837
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 53% (53/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.940000
obj = -1.295243, rho = 0.822732
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.940000
obj = -1.827256, rho = 0.745659
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.940000
obj = -2.554164, rho = 0.634143
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 53% (53/100) (classification)
Accuracy = 46.3% (463/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.940000
obj = -3.520397, rho = 0.473734
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 55% (55/100) (classification)
Accuracy = 46.8% (468/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -4.746013, rho = 0.242992
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 80% (80/100) (classification)
Accuracy = 72.1% (721/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.177288, rho = -0.006977
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.856470
obj = -7.811474, rho = -0.069653
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.767542
obj = -9.606830, rho = -0.099952
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.651147
obj = -11.622344, rho = -0.085415
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.551095
obj = -14.031409, rho = -0.130671
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.468349
obj = -16.796290, rho = -0.115084
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.385253
obj = -20.027475, rho = -0.169790
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.318238
obj = -23.961983, rho = -0.158407
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.270131
obj = -28.552935, rho = -0.237248
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.219761
obj = -33.943708, rho = -0.244073
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.185876
obj = -40.362854, rho = -0.171645
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.155367
obj = -47.631291, rho = -0.235193
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.127825
obj = -55.434031, rho = -0.529596
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.107082
obj = -62.856739, rho = -0.770563
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.898433, rho = 0.911021
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.278749, rho = 0.872008
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -1.811271, rho = 0.815889
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -2.547188, rho = 0.735166
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -3.543505, rho = 0.619050
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 54% (54/100) (classification)
Accuracy = 51.3% (513/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -4.847831, rho = 0.452022
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 59% (59/100) (classification)
Accuracy = 57.4% (574/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.457476, rho = 0.211762
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 86% (86/100) (classification)
Accuracy = 87% (870/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.866284
obj = -8.316438, rho = 0.142783
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 90% (90/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -10.506800, rho = 0.029010
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.715452
obj = -13.011540, rho = -0.025053
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.603376
obj = -15.953339, rho = -0.076006
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.522417
obj = -19.512039, rho = -0.120932
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.433594
obj = -23.814795, rho = -0.119092
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.369637
obj = -29.322228, rho = -0.131199
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.308542
obj = -36.510896, rho = -0.157022
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.265984
obj = -45.951164, rho = -0.142811
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.235619
obj = -58.081128, rho = -0.086407
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.206416
obj = -73.305628, rho = -0.119491
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.187105
obj = -91.486676, rho = -0.242559
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.163722
obj = -112.458112, rho = -0.241630
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -0.898915, rho = -0.922398
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -1.279748, rho = -0.889144
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -1.813340, rho = -0.840984
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -2.551468, rho = -0.771263
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -3.552361, rho = -0.670974
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 54% (54/100) (classification)
Accuracy = 51.4% (514/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -4.866155, rho = -0.526712
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 56% (56/100) (classification)
Accuracy = 55.4% (554/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -6.495391, rho = -0.319199
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 81% (81/100) (classification)
Accuracy = 81.6% (816/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -8.350863, rho = -0.085607
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.811838
obj = -10.387888, rho = 0.001730
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.716028
obj = -12.620212, rho = 0.069968
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.607836
obj = -15.050931, rho = 0.097921
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.513589
obj = -17.706619, rho = 0.009715
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.410257
obj = -20.718037, rho = -0.029944
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.335120
obj = -24.434874, rho = -0.084944
nSV = 35, nBSV = 32
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.276340
obj = -28.518312, rho = -0.108947
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.229576
obj = -32.994921, rho = -0.048105
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.182977
obj = -37.782161, rho = -0.080400
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.148279
obj = -43.088392, rho = -0.066592
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*....*
optimization finished, #iter = 589
nu = 0.124560
obj = -47.480092, rho = 0.042101
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*
optimization finished, #iter = 331
nu = 0.092142
obj = -50.875727, rho = 0.042287
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -0.785599, rho = -0.953081
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -1.120963, rho = -0.932510
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -1.593656, rho = -0.902918
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -2.253513, rho = -0.859195
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -3.161110, rho = -0.797460
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -4.380625, rho = -0.708656
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 60% (60/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -5.956852, rho = -0.581336
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 67% (67/100) (classification)
Accuracy = 57.6% (576/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -7.855904, rho = -0.397772
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 87% (87/100) (classification)
Accuracy = 88% (880/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760000
obj = -9.922366, rho = -0.203160
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 95% (95/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.664357
obj = -12.241321, rho = -0.184038
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.580686
obj = -14.972975, rho = -0.112512
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.498339
obj = -18.123189, rho = -0.109440
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.413757
obj = -21.673886, rho = -0.195253
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 168
nu = 0.344099
obj = -26.069062, rho = -0.190497
nSV = 38, nBSV = 28
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.282453
obj = -31.567978, rho = -0.162066
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.238692
obj = -38.562296, rho = -0.107457
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.205589
obj = -47.121029, rho = -0.127712
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.174068
obj = -56.558689, rho = -0.103528
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.143282
obj = -68.816735, rho = -0.160169
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.123828
obj = -83.274819, rho = -0.231109
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -0.953843, rho = 0.862202
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.355559, rho = 0.801785
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -1.915769, rho = 0.714878
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -2.685109, rho = 0.589866
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.2% (492/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -3.716255, rho = 0.410043
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 51% (51/100) (classification)
Accuracy = 49.9% (499/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -5.043263, rho = 0.151376
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 77% (77/100) (classification)
Accuracy = 75% (750/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -6.628808, rho = -0.220703
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -8.467705, rho = -0.191793
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.829718
obj = -10.560677, rho = -0.164453
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.714287
obj = -12.866384, rho = -0.140802
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.609002
obj = -15.551482, rho = -0.094112
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.507860
obj = -18.791085, rho = -0.089973
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.431329
obj = -22.613160, rho = -0.041355
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.362571
obj = -27.333235, rho = -0.072940
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.308291
obj = -32.600909, rho = 0.003714
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.251933
obj = -38.794611, rho = -0.068489
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.209394
obj = -46.476157, rho = -0.090183
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.177713
obj = -54.345294, rho = -0.087069
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.143494
obj = -63.499545, rho = -0.112913
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*
optimization finished, #iter = 264
nu = 0.114925
obj = -74.203475, rho = -0.189430
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -0.857465, rho = 0.886498
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.219209, rho = 0.836733
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -1.724364, rho = 0.765149
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -2.419563, rho = 0.662178
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.7% (507/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -3.354519, rho = 0.514060
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 56% (56/100) (classification)
Accuracy = 50.8% (508/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -4.564799, rho = 0.301000
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 68% (68/100) (classification)
Accuracy = 59.1% (591/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -6.027208, rho = -0.005477
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 89% (89/100) (classification)
Accuracy = 85.7% (857/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.828213
obj = -7.621354, rho = -0.203238
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.746412
obj = -9.437120, rho = -0.138076
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.641738
obj = -11.423473, rho = -0.139548
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.538614
obj = -13.801415, rho = -0.131487
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.457342
obj = -16.681483, rho = -0.146235
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.388795
obj = -19.950002, rho = -0.136707
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.322960
obj = -23.471937, rho = -0.235011
nSV = 37, nBSV = 27
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.265134
obj = -27.677831, rho = -0.231694
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.213763
obj = -32.572482, rho = -0.247475
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.176327
obj = -38.495842, rho = -0.261304
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.146467
obj = -45.503722, rho = -0.288444
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.116014
obj = -54.119686, rho = -0.271021
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.099946
obj = -64.715162, rho = -0.416000
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
No description has been provided for this image
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt


def data(N,sigma):   
    w = np.ones(10)/np.sqrt(10)   
    w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)   
    w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)   
    x = np.zeros((4,10))   
    x[1,:] = x[0,:] + sigma*w1   
    x[2,:] = x[0,:] + sigma*w2   
    x[3,:] = x[2,:] + sigma*w1   
    X1 = x + sigma*matlib.repmat(w,4,1)/2   
    X2 = x - sigma*matlib.repmat(w,4,1)/2   
    X1 = matlib.repmat(X1,2*N,1)   
    X2 = matlib.repmat(X2,2*N,1)   
    X = np.concatenate((X1, X2), axis=0)   
    Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)   
    Z = np.random.permutation(16*N)   
    Z = Z[:N]   
    X = X[Z,:]   
    X = X + 0.2*sigma*np.random.randn(N,10)   
    Y = Y[Z]

    return X, Y

# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-2, 1, 20)  # Logarithmically spaced values between 0.01 and 10

# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 3

# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):

    # Generate data
    X_train, y_train = data(100,sigma)
    X_test, y_test = data(1000, sigma)

    for lam in lambda_values:
        
        # Train SVM
        svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
        param = svm_parameter(f'-t 0 -c {lam}')
        model = svm_train(svm_problem_setup, param)
        
        # Predict on training and test data
        i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
        i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
        
        # Calculate errors
        training_errors.append(100 - train_accuracy[0])  # Convert to error percentage
        test_errors.append(100 - test_accuracy[0])  # Convert to error percentage

# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)

avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)

lambda_values_log = np.log10(lambda_values)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')

plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.01,10) @ σ = 3')
plt.legend()
plt.show()
*
optimization finished, #iter = 39
nu = 0.576530
obj = -0.380738, rho = -0.186063
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.468826
obj = -0.456413, rho = -0.252658
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.400000
obj = -0.551934, rho = -0.248054
nSV = 41, nBSV = 38
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.344768
obj = -0.653207, rho = -0.120397
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.280534
obj = -0.762595, rho = -0.133010
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.224157
obj = -0.894680, rho = -0.152303
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.188049
obj = -1.040691, rho = -0.003802
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.149157
obj = -1.211012, rho = -0.044550
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.121056
obj = -1.417628, rho = -0.089917
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.098831
obj = -1.654418, rho = -0.176546
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.079950
obj = -1.924867, rho = -0.201699
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.067801
obj = -2.198938, rho = -0.269724
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.053828
obj = -2.473454, rho = -0.443824
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.040549
obj = -2.776568, rho = -0.477961
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.032920
obj = -3.107947, rho = -0.707736
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.028139
obj = -3.349394, rho = -0.964501
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.019944
obj = -3.351027, rho = -0.988347
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.013865
obj = -3.351027, rho = -0.988347
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.009639
obj = -3.351027, rho = -0.988347
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.006701
obj = -3.351027, rho = -0.988347
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.643056
obj = -0.453552, rho = -0.132182
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.560649
obj = -0.557408, rho = -0.163104
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.477947
obj = -0.674138, rho = -0.139837
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.399071
obj = -0.818189, rho = -0.168112
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.347049
obj = -0.984933, rho = -0.093166
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.286316
obj = -1.178063, rho = -0.153143
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 268
nu = 0.233505
obj = -1.427395, rho = -0.151934
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.195211
obj = -1.750069, rho = -0.200979
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.166340
obj = -2.167855, rho = -0.200985
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.146797
obj = -2.666572, rho = -0.332962
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.125493
obj = -3.232415, rho = -0.329829
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.105646
obj = -3.934392, rho = -0.372136
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.090376
obj = -4.736589, rho = -0.443849
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 396
nu = 0.072122
obj = -5.777105, rho = -0.444015
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.061154
obj = -7.230499, rho = -0.506814
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.054119
obj = -9.058336, rho = -0.584263
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.046707
obj = -11.289067, rho = -0.670096
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.045317
obj = -13.570046, rho = -1.341721
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.039465
obj = -15.122191, rho = -2.322718
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 365
nu = 0.030758
obj = -15.384385, rho = -2.927288
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.544525
obj = -0.366421, rho = 0.073823
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.452980
obj = -0.444144, rho = 0.106953
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.386129
obj = -0.538560, rho = 0.106890
nSV = 40, nBSV = 38
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.327333
obj = -0.645472, rho = 0.113558
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.272803
obj = -0.770530, rho = 0.103517
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.232287
obj = -0.900773, rho = -0.000910
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*..*
optimization finished, #iter = 290
nu = 0.182434
obj = -1.054258, rho = 0.016440
nSV = 24, nBSV = 12
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.152324
obj = -1.246211, rho = 0.123407
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.123626
obj = -1.469840, rho = 0.023906
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.105120
obj = -1.712550, rho = -0.002827
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.084754
obj = -1.958905, rho = -0.040937
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.069559
obj = -2.188916, rho = -0.140359
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*..*
optimization finished, #iter = 217
nu = 0.054416
obj = -2.394191, rho = -0.166641
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.041789
obj = -2.589787, rho = -0.192524
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.032812
obj = -2.663932, rho = -0.190178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.022810
obj = -2.663932, rho = -0.190178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.015858
obj = -2.663932, rho = -0.190178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.011024
obj = -2.663932, rho = -0.190178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.007664
obj = -2.663932, rho = -0.190178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.005328
obj = -2.663932, rho = -0.190178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.573272
obj = -0.408033, rho = -0.015287
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.490721
obj = -0.509302, rho = -0.013793
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.449060
obj = -0.626329, rho = -0.025961
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.378006
obj = -0.754331, rho = -0.119488
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.312857
obj = -0.909570, rho = -0.189139
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.266968
obj = -1.098921, rho = -0.232045
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.224243
obj = -1.307717, rho = -0.241437
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.188291
obj = -1.546872, rho = -0.194333
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.157468
obj = -1.810299, rho = -0.157200
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.127681
obj = -2.098318, rho = -0.073678
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.101894
obj = -2.415995, rho = -0.088809
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.081776
obj = -2.794529, rho = -0.148316
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.071487
obj = -3.177793, rho = -0.170102
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.057916
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.040263
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.027990
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.019459
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.013528
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.009404
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.006538
obj = -3.269093, rho = -0.249425
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.563617
obj = -0.380668, rho = -0.273345
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.475539
obj = -0.458616, rho = -0.276606
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.396597
obj = -0.552278, rho = -0.238377
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.325454
obj = -0.672804, rho = -0.254199
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 20
nu = 0.279874
obj = -0.824080, rho = -0.295309
nSV = 28, nBSV = 26
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.250715
obj = -0.989928, rho = -0.240952
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.199581
obj = -1.163122, rho = -0.192792
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.163279
obj = -1.394392, rho = -0.144329
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.140938
obj = -1.647659, rho = 0.045634
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.117736
obj = -1.934771, rho = 0.082146
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.097970
obj = -2.199117, rho = 0.184533
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.076891
obj = -2.433176, rho = 0.192253
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.063313
obj = -2.593357, rho = 0.334502
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.046610
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.032403
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.022526
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.015660
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.010887
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.007568
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 430
nu = 0.005262
obj = -2.630766, rho = 0.202512
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.577399
obj = -0.402046, rho = -0.269095
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.502418
obj = -0.489812, rho = -0.288525
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.432414
obj = -0.590149, rho = -0.243619
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.361310
obj = -0.703952, rho = -0.197051
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.298707
obj = -0.830968, rho = -0.227861
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.244901
obj = -0.976858, rho = -0.261499
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.200267
obj = -1.160220, rho = -0.255126
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.166324
obj = -1.377935, rho = -0.354079
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 173
nu = 0.140820
obj = -1.603227, rho = -0.410725
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.113069
obj = -1.858056, rho = -0.312937
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.092077
obj = -2.141846, rho = -0.298591
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.073260
obj = -2.460373, rho = -0.419297
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.062709
obj = -2.765604, rho = -0.535065
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.048506
obj = -2.946359, rho = -0.436605
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.035753
obj = -3.081511, rho = -0.350033
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.026759
obj = -3.124828, rho = -0.199506
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.018603
obj = -3.124828, rho = -0.199506
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.012932
obj = -3.124828, rho = -0.199506
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.008991
obj = -3.124828, rho = -0.199506
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.006250
obj = -3.124828, rho = -0.199506
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.542329
obj = -0.365538, rho = -0.241672
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.456726
obj = -0.438866, rho = -0.223218
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.386356
obj = -0.526173, rho = -0.203462
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.318636
obj = -0.623174, rho = -0.308583
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.266084
obj = -0.734425, rho = -0.287974
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.216925
obj = -0.867552, rho = -0.239051
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.179710
obj = -1.029322, rho = -0.187873
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.150833
obj = -1.201583, rho = -0.219863
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.121669
obj = -1.390844, rho = -0.298128
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 114
nu = 0.102719
obj = -1.578892, rho = -0.380253
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*...*
optimization finished, #iter = 495
nu = 0.077801
obj = -1.763913, rho = -0.427335
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 286
nu = 0.059990
obj = -2.003207, rho = -0.476549
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.047513
obj = -2.295044, rho = -0.517095
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.039075
obj = -2.605993, rho = -0.582333
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 282
nu = 0.032567
obj = -2.849319, rho = -0.635022
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.024789
obj = -2.894969, rho = -0.608455
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.017233
obj = -2.894969, rho = -0.608455
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.011980
obj = -2.894969, rho = -0.608455
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.008329
obj = -2.894969, rho = -0.608455
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.005790
obj = -2.894969, rho = -0.608455
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.612798
obj = -0.428727, rho = -0.189171
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.523449
obj = -0.525374, rho = -0.251489
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.447406
obj = -0.647888, rho = -0.316988
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.389309
obj = -0.795563, rho = -0.386016
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.331459
obj = -0.971977, rho = -0.431387
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.286429
obj = -1.177791, rho = -0.541849
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.234959
obj = -1.416246, rho = -0.579656
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.204419
obj = -1.703187, rho = -0.542499
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.171452
obj = -2.027089, rho = -0.584158
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.142697
obj = -2.364915, rho = -0.607555
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*..*
optimization finished, #iter = 431
nu = 0.112638
obj = -2.758493, rho = -0.581017
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.089777
obj = -3.300831, rho = -0.589063
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.081059
obj = -3.897862, rho = -0.484512
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.067830
obj = -4.369573, rho = -0.595496
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.055446
obj = -4.553681, rho = -0.857277
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.039014
obj = -4.555328, rho = -0.809576
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.027122
obj = -4.555328, rho = -0.809576
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.018855
obj = -4.555328, rho = -0.809576
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.013108
obj = -4.555328, rho = -0.809576
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.009113
obj = -4.555328, rho = -0.809576
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.578498
obj = -0.398956, rho = -0.097378
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.497270
obj = -0.484097, rho = -0.155721
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.412990
obj = -0.588637, rho = -0.120077
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.343349
obj = -0.722569, rho = -0.173830
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.300000
obj = -0.887760, rho = -0.184033
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.252217
obj = -1.091093, rho = -0.200610
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.220194
obj = -1.333742, rho = -0.254444
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.187481
obj = -1.618183, rho = -0.290826
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.161277
obj = -1.939304, rho = -0.253138
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.137319
obj = -2.269107, rho = -0.276226
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 293
nu = 0.108950
obj = -2.629721, rho = -0.267108
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.086774
obj = -3.108839, rho = -0.239316
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.070598
obj = -3.727015, rho = -0.291036
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.062049
obj = -4.428380, rho = -0.573819
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.050679
obj = -5.180017, rho = -0.735989
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.043770
obj = -5.789674, rho = -0.680793
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.035023
obj = -6.175012, rho = -0.400481
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.025710
obj = -6.212664, rho = -0.282586
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.017874
obj = -6.212664, rho = -0.282586
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.012426
obj = -6.212664, rho = -0.282586
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.584212
obj = -0.377809, rho = -0.137232
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.485812
obj = -0.442069, rho = -0.156619
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 99.5% (995/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.393368
obj = -0.512820, rho = -0.202878
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.317952
obj = -0.598883, rho = -0.297630
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.259406
obj = -0.698376, rho = -0.265890
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.213445
obj = -0.802603, rho = -0.138684
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.167464
obj = -0.919093, rho = -0.148337
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.133697
obj = -1.057308, rho = -0.165371
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.105427
obj = -1.223188, rho = -0.168944
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.090667
obj = -1.406691, rho = -0.119817
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99.5% (995/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.073337
obj = -1.531851, rho = 0.068661
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.055498
obj = -1.612802, rho = 0.132072
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.041816
obj = -1.688613, rho = 0.102505
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.029969
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.020834
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.014484
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.010069
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.007000
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.004866
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.003383
obj = -1.691745, rho = 0.104250
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.605324
obj = -0.410105, rho = 0.100455
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.522263
obj = -0.491214, rho = 0.044501
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.429964
obj = -0.584445, rho = 0.054182
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.357946
obj = -0.691526, rho = 0.068705
nSV = 37, nBSV = 34
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.298522
obj = -0.809870, rho = 0.073266
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.238817
obj = -0.945613, rho = 0.052719
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.194853
obj = -1.108321, rho = 0.051647
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.156982
obj = -1.312605, rho = -0.006676
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 288
nu = 0.129875
obj = -1.558093, rho = -0.080708
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.110530
obj = -1.842055, rho = 0.000116
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.096574
obj = -2.088997, rho = 0.194137
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.076189
obj = -2.226801, rho = 0.133491
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.057845
obj = -2.314993, rho = 0.001439
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.041070
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.028552
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.019849
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.013799
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.009593
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.006669
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.004636
obj = -2.317605, rho = -0.023320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.601454
obj = -0.414109, rho = -0.335456
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.513809
obj = -0.501823, rho = -0.306952
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.427539
obj = -0.606428, rho = -0.332715
nSV = 48, nBSV = 38
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.360000
obj = -0.744175, rho = -0.331803
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.315212
obj = -0.903035, rho = -0.261642
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.257949
obj = -1.093550, rho = -0.269803
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.228188
obj = -1.319142, rho = -0.191402
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.193060
obj = -1.542652, rho = -0.218925
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.158890
obj = -1.781773, rho = -0.159889
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 242
nu = 0.123819
obj = -2.061518, rho = -0.148858
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 365
nu = 0.099469
obj = -2.388991, rho = -0.094636
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.078360
obj = -2.810329, rho = -0.097236
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 418
nu = 0.062840
obj = -3.388699, rho = -0.041757
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*
optimization finished, #iter = 385
nu = 0.053631
obj = -4.161041, rho = 0.050115
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 322
nu = 0.048834
obj = -4.990326, rho = 0.201602
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 338
nu = 0.039135
obj = -5.858226, rho = 0.191481
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 446
nu = 0.032046
obj = -6.806090, rho = 0.174880
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 297
nu = 0.025486
obj = -8.042718, rho = 0.199242
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.025153
obj = -8.923833, rho = 0.473530
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.017854
obj = -8.927766, rho = 0.493102
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.578043
obj = -0.410068, rho = -0.119980
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.492470
obj = -0.509557, rho = -0.155435
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.429645
obj = -0.635483, rho = -0.170687
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.379131
obj = -0.790433, rho = -0.251557
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.317903
obj = -0.981530, rho = -0.316964
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.279432
obj = -1.219276, rho = -0.306224
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.242543
obj = -1.512441, rho = -0.441978
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 22
nu = 0.215563
obj = -1.865296, rho = -0.414203
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.180879
obj = -2.242347, rho = -0.402874
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.154937
obj = -2.700646, rho = -0.364963
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.128485
obj = -3.223700, rho = -0.433711
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.110285
obj = -3.832328, rho = -0.636306
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.094130
obj = -4.400283, rho = -0.376550
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 311
nu = 0.071926
obj = -4.961488, rho = -0.285049
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*..*
optimization finished, #iter = 451
nu = 0.058729
obj = -5.621258, rho = -0.090472
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.045475
obj = -6.286417, rho = -0.143236
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.037218
obj = -6.964337, rho = -0.280160
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 275
nu = 0.029492
obj = -7.128910, rho = -0.363303
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
..*
optimization finished, #iter = 275
nu = 0.020503
obj = -7.128910, rho = -0.363303
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
..*
optimization finished, #iter = 275
nu = 0.014253
obj = -7.128910, rho = -0.363303
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.651185
obj = -0.450137, rho = 0.040456
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.554987
obj = -0.549983, rho = 0.032055
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.471748
obj = -0.668332, rho = 0.065344
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.403173
obj = -0.811816, rho = 0.054609
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.334994
obj = -0.990183, rho = 0.013783
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.282700
obj = -1.214225, rho = 0.026431
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.240382
obj = -1.489500, rho = 0.005739
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.211836
obj = -1.816562, rho = 0.080556
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.177051
obj = -2.197928, rho = 0.014448
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.153469
obj = -2.663100, rho = 0.197019
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.125289
obj = -3.172020, rho = 0.208541
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.106365
obj = -3.806120, rho = 0.071800
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.088273
obj = -4.504238, rho = 0.097493
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*....*
optimization finished, #iter = 623
nu = 0.073763
obj = -5.232581, rho = 0.396416
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
....*
optimization finished, #iter = 458
nu = 0.059347
obj = -6.103914, rho = 0.630529
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*.*
optimization finished, #iter = 339
nu = 0.046483
obj = -7.192755, rho = 0.692530
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 327
nu = 0.039681
obj = -8.487553, rho = 0.863090
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.035580
obj = -9.678765, rho = 0.920874
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.028381
obj = -9.867653, rho = 0.897722
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.019730
obj = -9.867653, rho = 0.897722
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.555610
obj = -0.368516, rho = -0.037081
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.451701
obj = -0.441959, rho = -0.051196
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.392210
obj = -0.532448, rho = 0.043257
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.318362
obj = -0.636396, rho = 0.044700
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.266492
obj = -0.766921, rho = -0.023839
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.230161
obj = -0.910836, rho = -0.064711
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.186665
obj = -1.071558, rho = -0.066886
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.157963
obj = -1.260688, rho = -0.064039
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.127336
obj = -1.452445, rho = -0.042031
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.107755
obj = -1.660960, rho = -0.099414
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.085557
obj = -1.817938, rho = -0.094092
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.063942
obj = -1.963765, rho = -0.091647
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 426
nu = 0.048317
obj = -2.125800, rho = -0.188296
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.036777
obj = -2.303711, rho = -0.279847
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.028904
obj = -2.347258, rho = -0.372788
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.020094
obj = -2.347258, rho = -0.372788
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.013969
obj = -2.347258, rho = -0.372788
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.009711
obj = -2.347258, rho = -0.372788
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.006751
obj = -2.347258, rho = -0.372788
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.004693
obj = -2.347258, rho = -0.372788
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.605016
obj = -0.435108, rho = -0.039178
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.532241
obj = -0.541491, rho = -0.034595
nSV = 54, nBSV = 52
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.458494
obj = -0.670780, rho = -0.088982
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.398214
obj = -0.830209, rho = -0.081997
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.341649
obj = -1.017081, rho = -0.144723
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.288802
obj = -1.248089, rho = -0.193627
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.246377
obj = -1.536195, rho = -0.123941
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.206196
obj = -1.915031, rho = -0.077219
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.184899
obj = -2.387386, rho = -0.094609
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.162333
obj = -2.931552, rho = -0.120687
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.134416
obj = -3.578200, rho = -0.094387
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.118004
obj = -4.404678, rho = -0.215324
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.096346
obj = -5.440307, rho = -0.233108
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.086567
obj = -6.717364, rho = -0.349087
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.071557
obj = -8.192973, rho = -0.395975
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.061739
obj = -10.182400, rho = -0.372372
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*...*
optimization finished, #iter = 657
nu = 0.055528
obj = -12.504246, rho = -0.393000
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
....*.*
optimization finished, #iter = 533
nu = 0.049398
obj = -14.718775, rho = -0.440545
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..........*........*
optimization finished, #iter = 1843
nu = 0.040118
obj = -16.780409, rho = -0.514892
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...........*......*
optimization finished, #iter = 1750
nu = 0.035387
obj = -18.451789, rho = -0.572563
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.613394
obj = -0.429930, rho = 0.079758
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 95% (95/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.528138
obj = -0.528582, rho = 0.068772
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 95% (95/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.452212
obj = -0.647083, rho = 0.028595
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 95% (95/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.386484
obj = -0.789908, rho = -0.063233
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.320000
obj = -0.971222, rho = -0.112728
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.276048
obj = -1.197973, rho = -0.144927
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.231598
obj = -1.492694, rho = -0.114584
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.204199
obj = -1.861291, rho = -0.077796
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.174697
obj = -2.301247, rho = -0.151241
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.148428
obj = -2.893339, rho = -0.171530
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.131510
obj = -3.674438, rho = -0.230817
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 376
nu = 0.115959
obj = -4.632299, rho = -0.191756
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 264
nu = 0.103511
obj = -5.845220, rho = -0.213750
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*...*
optimization finished, #iter = 581
nu = 0.097242
obj = -7.116042, rho = -0.255517
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.080586
obj = -8.300264, rho = -0.434351
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
......*
optimization finished, #iter = 654
nu = 0.069259
obj = -9.626915, rho = -0.524109
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*........*
optimization finished, #iter = 1321
nu = 0.057388
obj = -10.410877, rho = -0.721429
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
........*.*
optimization finished, #iter = 968
nu = 0.043697
obj = -10.559080, rho = -0.705594
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
........*.*
optimization finished, #iter = 968
nu = 0.030378
obj = -10.559080, rho = -0.705594
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
........*.*
optimization finished, #iter = 968
nu = 0.021119
obj = -10.559080, rho = -0.705594
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.561207
obj = -0.391070, rho = -0.008958
nSV = 61, nBSV = 52
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.480000
obj = -0.483036, rho = -0.070685
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.417548
obj = -0.589296, rho = -0.082125
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.353718
obj = -0.710587, rho = -0.023504
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.292027
obj = -0.861134, rho = -0.052207
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.250837
obj = -1.048806, rho = -0.073257
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.211704
obj = -1.269681, rho = -0.126721
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.181880
obj = -1.527419, rho = -0.085633
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.153065
obj = -1.795910, rho = 0.009087
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.126369
obj = -2.075247, rho = -0.095604
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 168
nu = 0.101483
obj = -2.398511, rho = -0.223290
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.081803
obj = -2.766243, rho = -0.165202
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.067950
obj = -3.173793, rho = -0.212579
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.057894
obj = -3.412045, rho = -0.338704
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.042534
obj = -3.452512, rho = -0.309643
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.029570
obj = -3.452512, rho = -0.309643
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.020557
obj = -3.452512, rho = -0.309643
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.014291
obj = -3.452512, rho = -0.309643
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.009935
obj = -3.452512, rho = -0.309643
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.006907
obj = -3.452512, rho = -0.309643
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.591133
obj = -0.396428, rho = -0.144244
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.496160
obj = -0.476658, rho = -0.094801
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.420844
obj = -0.571039, rho = -0.129300
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.353621
obj = -0.678035, rho = -0.088355
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.293794
obj = -0.793633, rho = -0.087663
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.240759
obj = -0.913450, rho = -0.127804
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 231
nu = 0.197554
obj = -1.037259, rho = -0.165355
nSV = 25, nBSV = 14
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.156906
obj = -1.159637, rho = -0.277985
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*...*
optimization finished, #iter = 468
nu = 0.119473
obj = -1.277844, rho = -0.316961
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 224
nu = 0.089417
obj = -1.433175, rho = -0.333024
nSV = 17, nBSV = 5
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.071646
obj = -1.634772, rho = -0.350053
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.058126
obj = -1.827050, rho = -0.277760
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.045038
obj = -1.991094, rho = -0.292242
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.036672
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.025494
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.017723
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.012321
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.008566
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.005955
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.004140
obj = -2.069982, rho = -0.212819
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.558731
obj = -0.382010, rho = -0.057282
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.464962
obj = -0.465891, rho = -0.057438
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.398514
obj = -0.571589, rho = -0.118672
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.341846
obj = -0.697964, rho = -0.062360
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.284129
obj = -0.855352, rho = -0.063018
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.241091
obj = -1.065013, rho = 0.021990
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.207666
obj = -1.335693, rho = 0.104735
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.177958
obj = -1.682865, rho = 0.112198
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.163962
obj = -2.100770, rho = 0.043176
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.142433
obj = -2.563414, rho = -0.007620
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.118895
obj = -3.124952, rho = -0.057218
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.102775
obj = -3.814270, rho = -0.096041
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.086729
obj = -4.601549, rho = -0.093518
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.076359
obj = -5.522394, rho = -0.204059
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*.*
optimization finished, #iter = 429
nu = 0.064739
obj = -6.323539, rho = -0.210509
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*...*
optimization finished, #iter = 775
nu = 0.049396
obj = -7.249417, rho = -0.266047
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.038858
obj = -8.524876, rho = -0.312307
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.033903
obj = -9.998858, rho = -0.975980
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 393
nu = 0.030994
obj = -10.775209, rho = -2.113943
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 393
nu = 0.021547
obj = -10.775209, rho = -2.113943
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.524831
obj = -0.359088, rho = -0.207957
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.445654
obj = -0.435431, rho = -0.147747
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.377758
obj = -0.524602, rho = -0.081235
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.319491
obj = -0.626260, rho = -0.011140
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.263377
obj = -0.747324, rho = -0.014302
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.219532
obj = -0.895330, rho = -0.091991
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.182647
obj = -1.076545, rho = -0.112458
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.156776
obj = -1.278008, rho = -0.224347
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.125459
obj = -1.496921, rho = -0.247816
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.100967
obj = -1.785365, rho = -0.246048
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.087133
obj = -2.128044, rho = -0.474997
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.072005
obj = -2.508701, rho = -0.548347
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.060476
obj = -2.936219, rho = -0.574873
nSV = 9, nBSV = 4
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.050908
obj = -3.268433, rho = -0.734196
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.041129
obj = -3.522397, rho = -0.909158
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.030317
obj = -3.541089, rho = -1.005954
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.021076
obj = -3.541089, rho = -1.005954
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.014652
obj = -3.541089, rho = -1.005954
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.010186
obj = -3.541089, rho = -1.005954
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.007081
obj = -3.541089, rho = -1.005954
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.601317
obj = -0.402181, rho = -0.037022
nSV = 63, nBSV = 55
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.501666
obj = -0.484178, rho = -0.060811
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.420129
obj = -0.582199, rho = -0.023040
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.353563
obj = -0.700675, rho = -0.035951
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.300000
obj = -0.837302, rho = 0.012238
nSV = 31, nBSV = 29
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.254690
obj = -0.983188, rho = 0.160788
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.204215
obj = -1.141112, rho = 0.174154
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.164110
obj = -1.325249, rho = 0.231082
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.133707
obj = -1.554265, rho = 0.242050
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.111716
obj = -1.800839, rho = 0.157757
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.090588
obj = -2.028625, rho = 0.265287
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.070782
obj = -2.269870, rho = 0.288202
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 114
nu = 0.058940
obj = -2.490094, rho = 0.278036
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.044596
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.031003
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.021553
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.014984
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.010416
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.007241
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.005034
obj = -2.517247, rho = 0.264514
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.638546
obj = -0.456251, rho = -0.339755
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 95% (95/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.565720
obj = -0.563677, rho = -0.278038
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.472013
obj = -0.694473, rho = -0.317923
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 95% (95/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.409054
obj = -0.858447, rho = -0.269050
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.355952
obj = -1.061296, rho = -0.328880
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.303174
obj = -1.299706, rho = -0.317388
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.258316
obj = -1.597466, rho = -0.328838
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.218952
obj = -1.966436, rho = -0.310161
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.181485
obj = -2.459354, rho = -0.343821
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.157234
obj = -3.128538, rho = -0.480849
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.137945
obj = -3.993831, rho = -0.572916
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 116
nu = 0.119619
obj = -5.177105, rho = -0.575958
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.109700
obj = -6.767988, rho = -0.791616
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.099991
obj = -8.768698, rho = -0.869198
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 96% (96/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
...*.*
optimization finished, #iter = 421
nu = 0.088196
obj = -11.442792, rho = -0.918529
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.077639
obj = -15.184516, rho = -0.868349
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.074283
obj = -20.323979, rho = -1.036993
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*.*
optimization finished, #iter = 165
nu = 0.073284
obj = -26.414410, rho = -1.402138
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
.*..*
optimization finished, #iter = 319
nu = 0.071269
obj = -32.472213, rho = -1.767580
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 92.7% (927/1000) (classification)
.....*....*
optimization finished, #iter = 999
nu = 0.060209
obj = -37.627131, rho = -1.882741
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.552108
obj = -0.370301, rho = -0.117666
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.463026
obj = -0.447318, rho = -0.147201
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.392498
obj = -0.537627, rho = -0.149850
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.327161
obj = -0.645707, rho = -0.137947
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.269414
obj = -0.772620, rho = -0.210829
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.223250
obj = -0.937480, rho = -0.241747
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.197311
obj = -1.120204, rho = -0.336576
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 207
nu = 0.160934
obj = -1.301590, rho = -0.258469
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.129866
obj = -1.518686, rho = -0.177237
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.110571
obj = -1.764137, rho = -0.174084
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.088631
obj = -1.957574, rho = -0.109418
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.068021
obj = -2.181997, rho = -0.255486
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.051928
obj = -2.434776, rho = -0.443059
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.040718
obj = -2.727589, rho = -0.741120
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.032599
obj = -3.064410, rho = -0.838859
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.027587
obj = -3.222462, rho = -0.984085
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.019178
obj = -3.222462, rho = -0.984085
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.013333
obj = -3.222462, rho = -0.984085
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.009269
obj = -3.222462, rho = -0.984085
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.006444
obj = -3.222462, rho = -0.984085
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.558296
obj = -0.376226, rho = -0.299078
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.471534
obj = -0.454166, rho = -0.305384
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.395669
obj = -0.543035, rho = -0.265481
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.329551
obj = -0.648857, rho = -0.316932
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.268246
obj = -0.784235, rho = -0.337693
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.223552
obj = -0.955002, rho = -0.388010
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.187985
obj = -1.171433, rho = -0.440076
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.165327
obj = -1.440787, rho = -0.807479
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.145937
obj = -1.728797, rho = -1.033220
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.121158
obj = -2.012745, rho = -1.226339
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.101088
obj = -2.283590, rho = -1.228041
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.080707
obj = -2.575074, rho = -1.204258
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.062368
obj = -2.861186, rho = -1.283697
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.049926
obj = -3.143449, rho = -1.400114
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.039609
obj = -3.216017, rho = -1.666100
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.027536
obj = -3.216017, rho = -1.666100
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.019143
obj = -3.216017, rho = -1.666100
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.013308
obj = -3.216017, rho = -1.666100
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.009252
obj = -3.216017, rho = -1.666100
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.006432
obj = -3.216017, rho = -1.666100
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.527480
obj = -0.363260, rho = -0.123328
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.460000
obj = -0.440701, rho = -0.114509
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.386225
obj = -0.520070, rho = -0.240432
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.314027
obj = -0.616532, rho = -0.274106
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.259847
obj = -0.734836, rho = -0.251620
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.210668
obj = -0.885077, rho = -0.264008
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.172512
obj = -1.084924, rho = -0.296913
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.150437
obj = -1.339317, rho = -0.404250
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.129263
obj = -1.652604, rho = -0.267711
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*..*
optimization finished, #iter = 378
nu = 0.110927
obj = -2.018822, rho = -0.199926
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
..*...*
optimization finished, #iter = 531
nu = 0.094351
obj = -2.471014, rho = -0.235785
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.080452
obj = -2.994062, rho = -0.320629
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.069602
obj = -3.616945, rho = -0.619514
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.057177
obj = -4.337821, rho = -0.669406
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.049433
obj = -5.158936, rho = -0.872500
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.040297
obj = -6.057045, rho = -0.984240
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.031670
obj = -7.233692, rho = -1.013864
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.027071
obj = -8.847877, rho = -1.223182
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.026131
obj = -10.199730, rho = -1.900386
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.020741
obj = -10.372549, rho = -2.243582
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.609670
obj = -0.405875, rho = -0.170207
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.508920
obj = -0.487345, rho = -0.125857
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.422149
obj = -0.583956, rho = -0.123477
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.356160
obj = -0.702294, rho = -0.091747
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.294322
obj = -0.841072, rho = -0.169761
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.240077
obj = -1.014566, rho = -0.157198
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.208526
obj = -1.226017, rho = -0.090085
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.175116
obj = -1.455215, rho = -0.031660
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.145003
obj = -1.712928, rho = -0.050908
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*
optimization finished, #iter = 363
nu = 0.122180
obj = -1.977056, rho = -0.070928
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.095174
obj = -2.281540, rho = -0.008798
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.077842
obj = -2.649215, rho = -0.025976
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 396
nu = 0.062231
obj = -3.056811, rho = -0.024937
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 236
nu = 0.050628
obj = -3.517047, rho = -0.100220
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.040731
obj = -4.041051, rho = -0.187046
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.035206
obj = -4.471017, rho = -0.345537
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*.*
optimization finished, #iter = 335
nu = 0.026950
obj = -4.527706, rho = -0.430732
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*.*
optimization finished, #iter = 335
nu = 0.018736
obj = -4.527706, rho = -0.430732
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*.*
optimization finished, #iter = 335
nu = 0.013025
obj = -4.527706, rho = -0.430732
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*.*
optimization finished, #iter = 335
nu = 0.009055
obj = -4.527706, rho = -0.430732
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.581658
obj = -0.396158, rho = -0.080347
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.493854
obj = -0.479823, rho = -0.030759
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.418654
obj = -0.582562, rho = -0.124796
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.358164
obj = -0.698335, rho = -0.062185
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.292040
obj = -0.829180, rho = -0.093733
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.239941
obj = -0.994969, rho = -0.099093
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.202491
obj = -1.196008, rho = -0.191433
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.170224
obj = -1.424968, rho = -0.350672
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.140664
obj = -1.691986, rho = -0.476303
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.118963
obj = -2.002934, rho = -0.577698
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.098008
obj = -2.341848, rho = -0.523963
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.077764
obj = -2.747978, rho = -0.580973
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.063236
obj = -3.270973, rho = -0.569143
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.051527
obj = -3.886896, rho = -0.510137
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.041713
obj = -4.723509, rho = -0.485376
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.034964
obj = -5.849157, rho = -0.458013
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
...*
optimization finished, #iter = 394
nu = 0.030052
obj = -7.323186, rho = -0.457711
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.......*....*
optimization finished, #iter = 1183
nu = 0.029074
obj = -8.876380, rho = -0.597076
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
........*.......*
optimization finished, #iter = 1520
nu = 0.027203
obj = -9.680529, rho = -0.810686
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
....*..........*
optimization finished, #iter = 1467
nu = 0.019375
obj = -9.688545, rho = -0.842633
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.554169
obj = -0.379244, rho = -0.066071
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.464120
obj = -0.463218, rho = -0.084984
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.394720
obj = -0.565066, rho = -0.061587
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.331436
obj = -0.695620, rho = -0.015373
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.283724
obj = -0.864057, rho = 0.023658
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.251459
obj = -1.068813, rho = -0.175891
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.211453
obj = -1.310899, rho = -0.139248
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.186238
obj = -1.594503, rho = -0.030085
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.158452
obj = -1.911963, rho = 0.062382
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.129718
obj = -2.288449, rho = 0.087475
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.104994
obj = -2.760595, rho = 0.055674
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.088979
obj = -3.414271, rho = 0.098611
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.079251
obj = -4.178481, rho = 0.041085
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.065641
obj = -5.000336, rho = -0.011803
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*...*
optimization finished, #iter = 620
nu = 0.055169
obj = -6.033884, rho = -0.063961
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.049882
obj = -7.178719, rho = -0.005065
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 363
nu = 0.044521
obj = -7.718051, rho = 0.074478
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
...*.*
optimization finished, #iter = 426
nu = 0.032024
obj = -7.738586, rho = 0.103963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
...*.*
optimization finished, #iter = 426
nu = 0.022263
obj = -7.738586, rho = 0.103963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
...*.*
optimization finished, #iter = 426
nu = 0.015477
obj = -7.738586, rho = 0.103963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.622985
obj = -0.424093, rho = -0.096250
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.534183
obj = -0.513690, rho = -0.019155
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.455863
obj = -0.611404, rho = 0.005453
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.373001
obj = -0.723118, rho = -0.041388
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.316572
obj = -0.849725, rho = 0.039749
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.258533
obj = -0.977277, rho = -0.017585
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.203154
obj = -1.117038, rho = 0.023516
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.165299
obj = -1.272183, rho = 0.003541
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.130040
obj = -1.444215, rho = -0.112094
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.105691
obj = -1.622291, rho = -0.084338
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*..*
optimization finished, #iter = 302
nu = 0.084797
obj = -1.752615, rho = -0.074069
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.062457
obj = -1.865386, rho = -0.074791
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*........*
optimization finished, #iter = 1035
nu = 0.045698
obj = -1.999904, rho = -0.118243
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*..*
optimization finished, #iter = 329
nu = 0.036123
obj = -2.136206, rho = -0.044191
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.026439
obj = -2.146581, rho = -0.006926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.018380
obj = -2.146581, rho = -0.006926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.012778
obj = -2.146581, rho = -0.006926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.008883
obj = -2.146581, rho = -0.006926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.006175
obj = -2.146581, rho = -0.006926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.004293
obj = -2.146581, rho = -0.006926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.595945
obj = -0.410896, rho = -0.101880
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.513260
obj = -0.499052, rho = -0.070218
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.439279
obj = -0.600779, rho = -0.132378
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.368678
obj = -0.713715, rho = -0.246754
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.304250
obj = -0.840940, rho = -0.288697
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.252116
obj = -0.992740, rho = -0.281686
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.204213
obj = -1.150510, rho = -0.217922
nSV = 25, nBSV = 14
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.164229
obj = -1.348544, rho = -0.255475
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.133121
obj = -1.595344, rho = -0.342039
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.109808
obj = -1.887852, rho = -0.490082
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.093723
obj = -2.209766, rho = -0.771409
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.073411
obj = -2.564131, rho = -0.774701
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.057488
obj = -3.048869, rho = -0.776313
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.047439
obj = -3.723622, rho = -0.838974
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.042518
obj = -4.541298, rho = -0.967858
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.039907
obj = -5.104337, rho = -1.106957
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.030989
obj = -5.206058, rho = -1.133109
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.021543
obj = -5.206058, rho = -1.133109
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.014977
obj = -5.206058, rho = -1.133109
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.010412
obj = -5.206058, rho = -1.133109
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.640000
obj = -0.435697, rho = -0.057903
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.550220
obj = -0.522702, rho = -0.089974
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.465214
obj = -0.620350, rho = -0.080063
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.380000
obj = -0.729627, rho = -0.136729
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.305190
obj = -0.865515, rho = -0.120331
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.254089
obj = -1.025479, rho = 0.019247
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.211149
obj = -1.215814, rho = 0.134329
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.175044
obj = -1.424628, rho = 0.151181
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.141679
obj = -1.680846, rho = 0.063994
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.119396
obj = -1.956683, rho = 0.037785
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.093465
obj = -2.258329, rho = 0.061558
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.077634
obj = -2.642117, rho = -0.005084
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.067579
obj = -2.966406, rho = -0.007922
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.053708
obj = -3.106795, rho = 0.167766
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.038345
obj = -3.113598, rho = 0.206899
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.026657
obj = -3.113598, rho = 0.206899
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.018532
obj = -3.113598, rho = 0.206899
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.012883
obj = -3.113598, rho = 0.206899
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.008956
obj = -3.113598, rho = 0.206899
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.006226
obj = -3.113598, rho = 0.206899
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.583602
obj = -0.404730, rho = -0.272512
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.510783
obj = -0.492102, rho = -0.255391
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.421466
obj = -0.596307, rho = -0.284495
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.365246
obj = -0.720363, rho = -0.265488
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.310426
obj = -0.857757, rho = -0.219810
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.256525
obj = -1.009444, rho = -0.264714
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*....*
optimization finished, #iter = 437
nu = 0.216863
obj = -1.166226, rho = -0.144983
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.172557
obj = -1.315238, rho = -0.116718
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.132261
obj = -1.493515, rho = -0.121331
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 344
nu = 0.105855
obj = -1.718918, rho = -0.078243
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*.*
optimization finished, #iter = 301
nu = 0.087268
obj = -1.959610, rho = -0.053251
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*...............*
optimization finished, #iter = 1623
nu = 0.067732
obj = -2.185193, rho = -0.151975
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*...*
optimization finished, #iter = 457
nu = 0.050941
obj = -2.476132, rho = -0.153776
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.043840
obj = -2.793916, rho = -0.236700
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.035633
obj = -2.893138, rho = -0.313233
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.024772
obj = -2.893138, rho = -0.313233
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.017221
obj = -2.893138, rho = -0.313233
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.011972
obj = -2.893138, rho = -0.313233
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.008323
obj = -2.893138, rho = -0.313233
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.005786
obj = -2.893138, rho = -0.313233
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.584530
obj = -0.411829, rho = -0.092869
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.503251
obj = -0.508733, rho = -0.072836
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.432438
obj = -0.628562, rho = -0.108276
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.384037
obj = -0.765320, rho = -0.175282
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.330922
obj = -0.906652, rho = -0.155861
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.268100
obj = -1.062591, rho = -0.131962
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.222041
obj = -1.252932, rho = -0.146878
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.181154
obj = -1.461091, rho = -0.107053
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 210
nu = 0.147138
obj = -1.707759, rho = -0.188190
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.119609
obj = -1.995697, rho = -0.241017
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 272
nu = 0.097709
obj = -2.306517, rho = -0.310099
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.078912
obj = -2.673708, rho = -0.335849
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.069380
obj = -2.993839, rho = -0.367893
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.054490
obj = -3.076001, rho = -0.463874
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.037885
obj = -3.076001, rho = -0.463960
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.026337
obj = -3.076001, rho = -0.463960
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.018310
obj = -3.076001, rho = -0.463960
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.012729
obj = -3.076001, rho = -0.463960
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.008849
obj = -3.076001, rho = -0.463960
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.006152
obj = -3.076001, rho = -0.463960
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.629106
obj = -0.438695, rho = -0.002875
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.540000
obj = -0.540615, rho = -0.033550
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.466988
obj = -0.659754, rho = 0.009879
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.393704
obj = -0.802957, rho = 0.037558
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.331789
obj = -0.982126, rho = 0.071360
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.281886
obj = -1.199164, rho = 0.089905
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.236897
obj = -1.465952, rho = 0.127920
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.198737
obj = -1.819788, rho = 0.178378
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.174140
obj = -2.262636, rho = 0.090133
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.148359
obj = -2.817657, rho = 0.041034
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.129507
obj = -3.507137, rho = 0.034357
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.108018
obj = -4.421264, rho = 0.085949
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.099642
obj = -5.604605, rho = 0.243539
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.095002
obj = -6.753752, rho = 0.420315
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.078224
obj = -7.667975, rho = 0.528450
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*..*
optimization finished, #iter = 497
nu = 0.065627
obj = -8.451132, rho = 0.410884
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
...*.*
optimization finished, #iter = 432
nu = 0.052322
obj = -8.788724, rho = 0.331544
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 432
nu = 0.036374
obj = -8.788724, rho = 0.331544
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 432
nu = 0.025287
obj = -8.788724, rho = 0.331544
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 432
nu = 0.017579
obj = -8.788724, rho = 0.331544
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.524083
obj = -0.341609, rho = -0.111286
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.427122
obj = -0.405585, rho = -0.101413
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.353895
obj = -0.483671, rho = -0.057691
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.289651
obj = -0.580352, rho = -0.020730
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 22
nu = 0.252053
obj = -0.693367, rho = -0.025930
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.200159
obj = -0.822848, rho = 0.002867
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.169414
obj = -0.984693, rho = 0.086659
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.140852
obj = -1.165601, rho = 0.009671
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.115318
obj = -1.383544, rho = -0.035319
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.099141
obj = -1.633160, rho = 0.025376
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.078124
obj = -1.894939, rho = 0.067368
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.066768
obj = -2.211549, rho = 0.046142
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.053996
obj = -2.474121, rho = 0.161290
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.044049
obj = -2.711533, rho = 0.073595
nSV = 7, nBSV = 2
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.033997
obj = -2.760099, rho = -0.104177
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.023635
obj = -2.760099, rho = -0.104177
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.016431
obj = -2.760099, rho = -0.104177
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.011422
obj = -2.760099, rho = -0.104177
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.007941
obj = -2.760099, rho = -0.104177
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.005520
obj = -2.760099, rho = -0.104177
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.631973
obj = -0.449430, rho = -0.215572
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.546930
obj = -0.555796, rho = -0.155049
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.475543
obj = -0.683839, rho = -0.099122
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.405785
obj = -0.836660, rho = -0.054046
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.341935
obj = -1.023462, rho = -0.024364
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.288238
obj = -1.262837, rho = -0.021406
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.252864
obj = -1.552487, rho = -0.079588
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.223943
obj = -1.886324, rho = -0.002520
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 289
nu = 0.185085
obj = -2.252426, rho = -0.004946
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*
optimization finished, #iter = 373
nu = 0.152223
obj = -2.704321, rho = -0.016201
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.122620
obj = -3.306495, rho = -0.013806
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.104663
obj = -4.116935, rho = -0.069332
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
...*
optimization finished, #iter = 382
nu = 0.087717
obj = -5.215903, rho = -0.056776
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 258
nu = 0.077621
obj = -6.686009, rho = -0.202394
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.070607
obj = -8.560678, rho = -0.294577
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*
optimization finished, #iter = 389
nu = 0.063892
obj = -10.739785, rho = -0.416786
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.....*...*
optimization finished, #iter = 873
nu = 0.054650
obj = -13.471599, rho = -0.601640
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.............*.*
optimization finished, #iter = 1503
nu = 0.045900
obj = -17.218674, rho = -0.647436
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
..........*..*
optimization finished, #iter = 1229
nu = 0.041464
obj = -22.186653, rho = -0.891580
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
..*
optimization finished, #iter = 244
nu = 0.038121
obj = -28.709163, rho = -1.267003
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.600000
obj = -0.415560, rho = -0.182027
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.531546
obj = -0.501426, rho = -0.142887
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.438656
obj = -0.594631, rho = -0.064442
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.360889
obj = -0.703416, rho = -0.004859
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.297446
obj = -0.836604, rho = -0.008505
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.244549
obj = -0.995059, rho = 0.001935
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.201984
obj = -1.186634, rho = 0.040445
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.171201
obj = -1.418584, rho = 0.153537
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.140959
obj = -1.678351, rho = 0.125893
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.118721
obj = -1.969861, rho = 0.169000
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 192
nu = 0.096268
obj = -2.265637, rho = 0.066130
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.077317
obj = -2.611724, rho = 0.146828
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.064051
obj = -2.964978, rho = 0.126946
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.052645
obj = -3.256215, rho = 0.113028
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.041592
obj = -3.377070, rho = 0.090938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.028915
obj = -3.377070, rho = 0.090938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.020101
obj = -3.377070, rho = 0.090938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.013974
obj = -3.377070, rho = 0.090938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.009715
obj = -3.377070, rho = 0.090938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.006754
obj = -3.377070, rho = 0.090938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.569994
obj = -0.380552, rho = 0.057095
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.473910
obj = -0.457486, rho = 0.070029
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.393051
obj = -0.550749, rho = 0.099188
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.339932
obj = -0.661555, rho = 0.224186
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.279031
obj = -0.785386, rho = 0.227333
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.229159
obj = -0.934526, rho = 0.197059
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.194984
obj = -1.109143, rho = 0.126664
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 273
nu = 0.160160
obj = -1.290884, rho = 0.142485
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.131110
obj = -1.505877, rho = 0.117019
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.111783
obj = -1.715949, rho = 0.018529
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.086030
obj = -1.869623, rho = -0.048606
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.068342
obj = -2.021771, rho = -0.067306
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.049702
obj = -2.141974, rho = -0.085752
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 242
nu = 0.038178
obj = -2.257408, rho = -0.147212
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.028310
obj = -2.298547, rho = -0.125119
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.019681
obj = -2.298547, rho = -0.125119
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.013682
obj = -2.298547, rho = -0.125119
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.009512
obj = -2.298547, rho = -0.125119
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.006612
obj = -2.298547, rho = -0.125119
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.004597
obj = -2.298547, rho = -0.125119
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.613539
obj = -0.431123, rho = -0.036399
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.532570
obj = -0.529054, rho = -0.036647
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.460284
obj = -0.638577, rho = -0.022850
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.386352
obj = -0.768095, rho = -0.051868
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.325558
obj = -0.914221, rho = -0.063307
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.264216
obj = -1.094384, rho = -0.081811
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.228695
obj = -1.291700, rho = -0.120817
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.187362
obj = -1.509992, rho = -0.059239
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*..*
optimization finished, #iter = 224
nu = 0.156316
obj = -1.749957, rho = -0.035387
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
..*..*
optimization finished, #iter = 485
nu = 0.122469
obj = -1.998443, rho = -0.138436
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.100710
obj = -2.292578, rho = -0.143112
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.086148
obj = -2.506796, rho = -0.291758
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 224
nu = 0.063360
obj = -2.591176, rho = -0.305963
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.046119
obj = -2.659093, rho = -0.362587
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.032773
obj = -2.661096, rho = -0.332223
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.022784
obj = -2.661096, rho = -0.332223
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.015839
obj = -2.661096, rho = -0.332223
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.011011
obj = -2.661096, rho = -0.332223
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.007655
obj = -2.661096, rho = -0.332223
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.005322
obj = -2.661096, rho = -0.332223
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.606520
obj = -0.418767, rho = -0.156417
nSV = 64, nBSV = 57
Total nSV = 64
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.525258
obj = -0.511351, rho = -0.147891
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.442606
obj = -0.617339, rho = -0.217417
nSV = 50, nBSV = 41
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.370118
obj = -0.746498, rho = -0.229413
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.310188
obj = -0.901108, rho = -0.257026
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*..*
optimization finished, #iter = 231
nu = 0.258918
obj = -1.090511, rho = -0.279820
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.220200
obj = -1.333663, rho = -0.348980
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.181118
obj = -1.632195, rho = -0.343678
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.155759
obj = -2.017913, rho = -0.385405
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.136767
obj = -2.488400, rho = -0.351444
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.117041
obj = -3.027745, rho = -0.230607
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.097864
obj = -3.704288, rho = -0.006927
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.082146
obj = -4.556239, rho = 0.034387
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.073919
obj = -5.609204, rho = 0.215809
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.065983
obj = -6.579685, rho = 0.216010
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.055319
obj = -7.336535, rho = -0.031479
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.043312
obj = -8.054972, rho = 0.029220
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.033801
obj = -8.169855, rho = 0.073014
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.023498
obj = -8.169855, rho = 0.073014
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.016336
obj = -8.169855, rho = 0.073014
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.577589
obj = -0.378037, rho = -0.078436
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.485965
obj = -0.445078, rho = -0.103421
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.406685
obj = -0.515330, rho = -0.130670
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.323020
obj = -0.587681, rho = -0.099377
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.259498
obj = -0.669719, rho = -0.062944
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.209221
obj = -0.750234, rho = -0.057552
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.164658
obj = -0.818521, rho = -0.066967
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.124051
obj = -0.887873, rho = -0.152711
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.096128
obj = -0.944813, rho = -0.146287
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 260
nu = 0.069762
obj = -0.997937, rho = -0.171607
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.050549
obj = -1.064389, rho = -0.163496
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*...*
optimization finished, #iter = 314
nu = 0.037959
obj = -1.142514, rho = -0.203179
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.029662
obj = -1.208560, rho = -0.418481
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.021473
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.014928
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.010378
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.007215
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.005016
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.003487
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.002424
obj = -1.212055, rho = -0.481487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.620000
obj = -0.414843, rho = -0.064482
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.520000
obj = -0.499812, rho = -0.036224
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.430294
obj = -0.598841, rho = -0.020308
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.355193
obj = -0.725205, rho = -0.000986
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.302027
obj = -0.883633, rho = 0.029077
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.257845
obj = -1.064369, rho = -0.086710
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.218112
obj = -1.272486, rho = -0.154330
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.178040
obj = -1.523587, rho = -0.217275
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.152510
obj = -1.819182, rho = -0.386655
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
....*...*
optimization finished, #iter = 747
nu = 0.125756
obj = -2.147094, rho = -0.524689
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.105326
obj = -2.516526, rho = -0.626848
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.087547
obj = -2.918008, rho = -0.586270
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.070905
obj = -3.324410, rho = -0.592314
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.055040
obj = -3.733241, rho = -0.647654
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.043590
obj = -4.162655, rho = -0.676269
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.037339
obj = -4.399978, rho = -0.914130
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.026250
obj = -4.410145, rho = -0.926616
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.018249
obj = -4.410145, rho = -0.926616
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.012687
obj = -4.410145, rho = -0.926616
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.008820
obj = -4.410145, rho = -0.926616
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.548782
obj = -0.385221, rho = -0.277984
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.468894
obj = -0.476004, rho = -0.230311
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.413665
obj = -0.581617, rho = -0.148136
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.360000
obj = -0.700588, rho = -0.242327
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*...*
optimization finished, #iter = 304
nu = 0.295074
obj = -0.831876, rho = -0.247121
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.237563
obj = -1.004522, rho = -0.250596
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.200886
obj = -1.223420, rho = -0.320784
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.169349
obj = -1.491790, rho = -0.325064
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.140859
obj = -1.841564, rho = -0.313303
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.124891
obj = -2.287485, rho = -0.352067
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.109991
obj = -2.759879, rho = -0.414890
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.091073
obj = -3.268815, rho = -0.468065
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*....*
optimization finished, #iter = 530
nu = 0.078143
obj = -3.857641, rho = -0.501731
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*.*
optimization finished, #iter = 558
nu = 0.066127
obj = -4.364634, rho = -0.491036
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.051141
obj = -4.860900, rho = -0.480826
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
...*.*
optimization finished, #iter = 403
nu = 0.041073
obj = -5.284176, rho = -0.437119
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 256
nu = 0.032688
obj = -5.491919, rho = -0.396707
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*
optimization finished, #iter = 256
nu = 0.022724
obj = -5.491919, rho = -0.396707
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*
optimization finished, #iter = 256
nu = 0.015798
obj = -5.491919, rho = -0.396707
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*
optimization finished, #iter = 256
nu = 0.010983
obj = -5.491919, rho = -0.396707
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.550669
obj = -0.373031, rho = -0.078639
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.460812
obj = -0.454055, rho = 0.012261
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.390621
obj = -0.551611, rho = -0.067051
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.328548
obj = -0.674061, rho = -0.004920
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.284595
obj = -0.818110, rho = 0.000112
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.241010
obj = -0.979249, rho = -0.070484
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.199346
obj = -1.165100, rho = -0.083356
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.166545
obj = -1.386787, rho = -0.079664
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.135683
obj = -1.654847, rho = -0.085558
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*...*
optimization finished, #iter = 415
nu = 0.112544
obj = -1.978541, rho = -0.034569
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.095608
obj = -2.380637, rho = -0.112717
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.078543
obj = -2.831885, rho = -0.096797
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
...*
optimization finished, #iter = 321
nu = 0.064391
obj = -3.368576, rho = -0.103738
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*...*
optimization finished, #iter = 403
nu = 0.051418
obj = -4.100913, rho = -0.113152
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*
optimization finished, #iter = 266
nu = 0.043697
obj = -5.112028, rho = -0.180407
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.041900
obj = -6.195224, rho = -0.610312
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.037004
obj = -7.001452, rho = -0.873471
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.029545
obj = -7.141204, rho = -1.127664
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.020540
obj = -7.141204, rho = -1.127664
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.014279
obj = -7.141204, rho = -1.127664
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.626723
obj = -0.416496, rho = -0.167249
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.520502
obj = -0.499179, rho = -0.183629
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.438868
obj = -0.591345, rho = -0.140053
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.360105
obj = -0.699169, rho = -0.121682
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.293952
obj = -0.835320, rho = -0.085175
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.242208
obj = -1.002543, rho = -0.137840
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.207402
obj = -1.202877, rho = -0.130526
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.172338
obj = -1.417294, rho = -0.116700
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.139140
obj = -1.679816, rho = -0.084481
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.113110
obj = -2.003880, rho = -0.055656
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.093254
obj = -2.443148, rho = -0.010947
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.082809
obj = -2.966656, rho = -0.139076
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.070901
obj = -3.499866, rho = -0.195028
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 265
nu = 0.056026
obj = -4.062726, rho = -0.225036
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.045312
obj = -4.812438, rho = -0.343040
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
...*.................*
optimization finished, #iter = 2049
nu = 0.037644
obj = -5.710134, rho = -0.555282
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.032892
obj = -6.669215, rho = -0.835956
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.029221
obj = -7.062593, rho = -1.116250
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.020314
obj = -7.062593, rho = -1.116250
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.014122
obj = -7.062593, rho = -1.116250
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.583683
obj = -0.387642, rho = -0.250995
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.482142
obj = -0.464267, rho = -0.283726
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.403556
obj = -0.558889, rho = -0.284002
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.340000
obj = -0.668575, rho = -0.252754
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.284031
obj = -0.787900, rho = -0.201191
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.238059
obj = -0.923449, rho = -0.132122
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.190065
obj = -1.080338, rho = -0.128566
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.153692
obj = -1.269459, rho = -0.149126
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.124411
obj = -1.510817, rho = -0.136292
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.105982
obj = -1.793201, rho = -0.182785
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.085710
obj = -2.088217, rho = -0.202617
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.067209
obj = -2.487721, rho = -0.201177
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.054947
obj = -3.050464, rho = -0.138278
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.050038
obj = -3.748358, rho = 0.020893
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.041780
obj = -4.395020, rho = 0.115470
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.033427
obj = -5.258174, rho = 0.137824
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.028716
obj = -6.327182, rho = 0.214633
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.026243
obj = -7.269817, rho = 0.231339
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.021477
obj = -7.467619, rho = 0.216438
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.014930
obj = -7.467619, rho = 0.216438
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.540761
obj = -0.370969, rho = -0.186916
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.475098
obj = -0.447243, rho = -0.184531
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.394181
obj = -0.531984, rho = -0.170048
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.328676
obj = -0.628989, rho = -0.084916
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.263320
obj = -0.743899, rho = -0.128661
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.220941
obj = -0.879744, rho = -0.221495
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.182630
obj = -1.037890, rho = -0.302779
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.151483
obj = -1.200014, rho = -0.344862
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.122827
obj = -1.377913, rho = -0.260478
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 113
nu = 0.099610
obj = -1.558475, rho = -0.213205
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*
optimization finished, #iter = 359
nu = 0.076803
obj = -1.740921, rho = -0.197749
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*.*
optimization finished, #iter = 692
nu = 0.060282
obj = -1.958681, rho = -0.109280
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*..*
optimization finished, #iter = 559
nu = 0.047066
obj = -2.213607, rho = -0.030379
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 321
nu = 0.036365
obj = -2.509897, rho = -0.028627
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.031567
obj = -2.802063, rho = 0.093679
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.024284
obj = -2.836377, rho = 0.183387
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.016882
obj = -2.836377, rho = 0.183387
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.011736
obj = -2.836377, rho = 0.183387
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.008159
obj = -2.836377, rho = 0.183387
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.005672
obj = -2.836377, rho = 0.183387
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.632683
obj = -0.455437, rho = -0.222760
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.551465
obj = -0.566334, rho = -0.152548
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.483628
obj = -0.699838, rho = -0.177206
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.411207
obj = -0.861635, rho = -0.122798
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.354735
obj = -1.062610, rho = -0.032681
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.303614
obj = -1.305040, rho = -0.024360
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.260000
obj = -1.615232, rho = -0.091892
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.219779
obj = -2.002825, rho = -0.130084
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.192296
obj = -2.485973, rho = -0.159316
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.163467
obj = -3.107975, rho = -0.261258
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.153471
obj = -3.778903, rho = -0.550073
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
......*.*
optimization finished, #iter = 718
nu = 0.122517
obj = -4.487674, rho = -0.546570
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*
optimization finished, #iter = 299
nu = 0.101834
obj = -5.464075, rho = -0.603333
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*..............*
optimization finished, #iter = 1654
nu = 0.084683
obj = -6.649993, rho = -0.748120
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.....*..................*
optimization finished, #iter = 2374
nu = 0.073159
obj = -8.118793, rho = -0.782929
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.060513
obj = -10.000898, rho = -0.726810
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.052834
obj = -12.337204, rho = -0.722613
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.048974
obj = -14.711111, rho = -1.005594
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 264
nu = 0.039212
obj = -17.022146, rho = -0.774956
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 315
nu = 0.032473
obj = -19.497329, rho = -0.625275
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.626084
obj = -0.438042, rho = -0.336419
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.537259
obj = -0.540218, rho = -0.326493
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.453410
obj = -0.665449, rho = -0.307008
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.385638
obj = -0.828585, rho = -0.356658
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.336570
obj = -1.036565, rho = -0.395579
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.297128
obj = -1.294038, rho = -0.304720
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.251019
obj = -1.601233, rho = -0.364898
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.213349
obj = -2.018766, rho = -0.386314
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.195384
obj = -2.546095, rho = -0.284675
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.164469
obj = -3.174227, rho = -0.254022
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.145111
obj = -4.023961, rho = -0.180342
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.134667
obj = -4.961623, rho = -0.350623
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.112573
obj = -5.962803, rho = -0.445529
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*....*
optimization finished, #iter = 759
nu = 0.100002
obj = -7.023250, rho = -0.410523
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.......*.*
optimization finished, #iter = 894
nu = 0.079958
obj = -8.035627, rho = -0.247998
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*..*
optimization finished, #iter = 577
nu = 0.062946
obj = -9.368491, rho = -0.300818
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*.*
optimization finished, #iter = 440
nu = 0.053741
obj = -10.788305, rho = -0.493645
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...............*.*
optimization finished, #iter = 1606
nu = 0.044435
obj = -11.753695, rho = -0.478742
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
...................*
optimization finished, #iter = 1979
nu = 0.032241
obj = -12.617811, rho = -0.448132
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
................*.......*
optimization finished, #iter = 2318
nu = 0.026309
obj = -13.155620, rho = -0.405874
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.579462
obj = -0.392937, rho = -0.109979
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.478695
obj = -0.478068, rho = -0.140163
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.403864
obj = -0.586854, rho = -0.087923
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.344031
obj = -0.727375, rho = -0.193363
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.300161
obj = -0.893837, rho = -0.147969
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.257271
obj = -1.095959, rho = -0.248108
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.217763
obj = -1.347292, rho = -0.205816
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.190701
obj = -1.641477, rho = -0.036750
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.164022
obj = -1.968001, rho = -0.090531
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.134808
obj = -2.334401, rho = -0.103589
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.110330
obj = -2.765724, rho = -0.216648
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 351
nu = 0.088600
obj = -3.329431, rho = -0.172723
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.077387
obj = -4.067678, rho = -0.151019
nSV = 11, nBSV = 6
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 173
nu = 0.068585
obj = -4.707245, rho = -0.090864
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.053279
obj = -5.385852, rho = -0.493143
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.041929
obj = -6.269153, rho = -0.721770
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.037216
obj = -7.278239, rho = -1.136549
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.031079
obj = -7.511594, rho = -1.556324
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.021606
obj = -7.511594, rho = -1.556324
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.015020
obj = -7.511594, rho = -1.556324
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.632383
obj = -0.419967, rho = 0.068433
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.534143
obj = -0.497390, rho = 0.018367
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.441383
obj = -0.581260, rho = 0.052367
nSV = 50, nBSV = 41
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.363472
obj = -0.676915, rho = 0.024907
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.291160
obj = -0.784573, rho = 0.122249
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.231576
obj = -0.915596, rho = 0.204664
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 203
nu = 0.183139
obj = -1.081755, rho = 0.235320
nSV = 27, nBSV = 15
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.151335
obj = -1.303016, rho = 0.180994
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.124270
obj = -1.595438, rho = 0.220809
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.110026
obj = -1.951616, rho = 0.580180
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.099722
obj = -2.263883, rho = 0.836316
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.081510
obj = -2.505440, rho = 1.084516
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 315
nu = 0.063640
obj = -2.645970, rho = 1.115858
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.047519
obj = -2.728125, rho = 1.040931
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.033632
obj = -2.731057, rho = 1.026477
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.023381
obj = -2.731057, rho = 1.026477
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.016254
obj = -2.731057, rho = 1.026477
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.011300
obj = -2.731057, rho = 1.026477
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.007856
obj = -2.731057, rho = 1.026477
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.005461
obj = -2.731057, rho = 1.026477
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.615690
obj = -0.421076, rho = -0.129992
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.530649
obj = -0.507562, rho = -0.040618
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.448212
obj = -0.604941, rho = -0.097888
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.371703
obj = -0.711750, rho = -0.157947
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.300339
obj = -0.838570, rho = -0.155334
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.248930
obj = -0.992501, rho = -0.071526
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.213848
obj = -1.162657, rho = -0.180977
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.170655
obj = -1.332074, rho = -0.153419
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*....*
optimization finished, #iter = 553
nu = 0.139980
obj = -1.504797, rho = -0.053683
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*.*
optimization finished, #iter = 318
nu = 0.113160
obj = -1.651959, rho = 0.061040
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*.*
optimization finished, #iter = 321
nu = 0.086915
obj = -1.744615, rho = 0.086216
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.063994
obj = -1.784449, rho = 0.075942
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.....*..*
optimization finished, #iter = 730
nu = 0.045447
obj = -1.794065, rho = 0.048741
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.031832
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.022130
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.015384
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.010695
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.007435
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.005169
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
........*....*
optimization finished, #iter = 1281
nu = 0.003593
obj = -1.797055, rho = 0.047946
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.590739
obj = -0.415806, rho = -0.315601
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.512577
obj = -0.510097, rho = -0.334349
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.439406
obj = -0.623682, rho = -0.258921
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.376084
obj = -0.755974, rho = -0.240311
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.318339
obj = -0.913902, rho = -0.222344
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.263704
obj = -1.103314, rho = -0.194921
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.229030
obj = -1.320120, rho = -0.142656
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.190436
obj = -1.549611, rho = -0.291364
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.157560
obj = -1.801478, rho = -0.286865
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.124897
obj = -2.090898, rho = -0.307760
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.103972
obj = -2.395098, rho = -0.316683
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.083386
obj = -2.735454, rho = -0.290869
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.066443
obj = -3.052328, rho = -0.201908
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.052457
obj = -3.403110, rho = -0.096304
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.043218
obj = -3.608395, rho = 0.101703
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.030949
obj = -3.615085, rho = 0.137744
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.021516
obj = -3.615085, rho = 0.137744
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.014958
obj = -3.615085, rho = 0.137744
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.010398
obj = -3.615085, rho = 0.137744
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.007229
obj = -3.615085, rho = 0.137744
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.607754
obj = -0.396654, rho = -0.184897
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.498163
obj = -0.467968, rho = -0.214615
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.414899
obj = -0.550329, rho = -0.192976
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.345966
obj = -0.645105, rho = -0.240493
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.280299
obj = -0.746674, rho = -0.259007
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.226464
obj = -0.858689, rho = -0.297959
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.180553
obj = -0.987436, rho = -0.258998
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.145266
obj = -1.132959, rho = -0.355497
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.117789
obj = -1.275108, rho = -0.465811
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.093728
obj = -1.426929, rho = -0.420603
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.071620
obj = -1.558668, rho = -0.426303
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 382
nu = 0.055961
obj = -1.713396, rho = -0.381732
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 283
nu = 0.042112
obj = -1.842308, rho = -0.373634
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*.*
optimization finished, #iter = 480
nu = 0.032741
obj = -1.974138, rho = -0.328896
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*..*
optimization finished, #iter = 535
nu = 0.024522
obj = -1.991348, rho = -0.312215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 535
nu = 0.017048
obj = -1.991348, rho = -0.312215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 535
nu = 0.011852
obj = -1.991348, rho = -0.312215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 535
nu = 0.008239
obj = -1.991348, rho = -0.312215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 535
nu = 0.005728
obj = -1.991348, rho = -0.312215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 535
nu = 0.003982
obj = -1.991348, rho = -0.312215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.577403
obj = -0.400362, rho = -0.161668
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.490139
obj = -0.487868, rho = -0.108003
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.418835
obj = -0.598223, rho = -0.113306
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.352316
obj = -0.733068, rho = -0.129722
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.302200
obj = -0.903311, rho = -0.170067
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.252714
obj = -1.111994, rho = -0.194329
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.219120
obj = -1.381426, rho = -0.211089
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.185649
obj = -1.716781, rho = -0.218258
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.161309
obj = -2.146696, rho = -0.337200
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.137910
obj = -2.711546, rho = -0.279108
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.119626
obj = -3.461499, rho = -0.206616
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.111021
obj = -4.427040, rho = -0.306186
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.106344
obj = -5.412955, rho = -0.306773
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.089236
obj = -6.236288, rho = -0.308561
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.070893
obj = -7.199684, rho = -0.431611
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.061132
obj = -8.122260, rho = -0.665200
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*
optimization finished, #iter = 382
nu = 0.050458
obj = -8.478904, rho = -0.903175
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*
optimization finished, #iter = 382
nu = 0.035078
obj = -8.478904, rho = -0.903175
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*
optimization finished, #iter = 382
nu = 0.024386
obj = -8.478904, rho = -0.903175
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*
optimization finished, #iter = 382
nu = 0.016953
obj = -8.478904, rho = -0.903175
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.596038
obj = -0.391857, rho = -0.229818
nSV = 64, nBSV = 56
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.496324
obj = -0.466774, rho = -0.254766
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.414899
obj = -0.554853, rho = -0.247339
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.342874
obj = -0.655205, rho = -0.178092
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.284282
obj = -0.759134, rho = -0.131330
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.226540
obj = -0.881272, rho = -0.104460
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.184249
obj = -1.028930, rho = -0.078520
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.150361
obj = -1.186004, rho = -0.099879
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.123847
obj = -1.350789, rho = -0.135975
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.**.*
optimization finished, #iter = 198
nu = 0.096403
obj = -1.506211, rho = -0.186095
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*.*
optimization finished, #iter = 275
nu = 0.073643
obj = -1.693431, rho = -0.206409
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.060455
obj = -1.914484, rho = -0.170034
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.049084
obj = -2.058099, rho = -0.167863
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.036951
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.025688
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.017858
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.012415
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.008631
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.006000
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.004171
obj = -2.085818, rho = -0.252013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.565268
obj = -0.385535, rho = -0.203249
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.480744
obj = -0.467333, rho = -0.131727
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.414122
obj = -0.556800, rho = -0.067158
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.345144
obj = -0.657576, rho = -0.036639
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.280685
obj = -0.769834, rho = 0.013033
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.230829
obj = -0.902407, rho = 0.026758
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.189947
obj = -1.049155, rho = 0.083043
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.154839
obj = -1.212102, rho = 0.150609
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.125415
obj = -1.365627, rho = 0.245594
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.099611
obj = -1.508361, rho = 0.222370
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.079439
obj = -1.634992, rho = 0.210087
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 168
nu = 0.058977
obj = -1.724544, rho = 0.204198
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.043027
obj = -1.806053, rho = 0.183845
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.032924
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.022889
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.015912
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.011062
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.007690
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.005346
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.003717
obj = -1.858303, rho = 0.161519
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.560855
obj = -0.373226, rho = 0.159690
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.469529
obj = -0.447769, rho = 0.144605
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.395096
obj = -0.533538, rho = 0.085575
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.328662
obj = -0.625978, rho = 0.114141
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.271674
obj = -0.735984, rho = 0.157475
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.220410
obj = -0.857501, rho = 0.175123
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.181466
obj = -0.980402, rho = 0.151550
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.143615
obj = -1.115529, rho = 0.122236
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.115148
obj = -1.275985, rho = 0.226692
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.093818
obj = -1.425247, rho = 0.243107
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.075394
obj = -1.520531, rho = 0.063933
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*.*
optimization finished, #iter = 394
nu = 0.055312
obj = -1.571035, rho = 0.038807
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.039462
obj = -1.616338, rho = 0.035894
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.029301
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.020370
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.014161
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.009845
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.006844
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.004758
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.003308
obj = -1.654118, rho = 0.087684
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.612608
obj = -0.401921, rho = -0.224178
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.513757
obj = -0.478024, rho = -0.256156
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.419634
obj = -0.564868, rho = -0.260755
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.343910
obj = -0.668912, rho = -0.221315
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.291251
obj = -0.791572, rho = -0.241141
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.235281
obj = -0.928808, rho = -0.289051
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.190038
obj = -1.092449, rho = -0.383331
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.156130
obj = -1.288838, rho = -0.394049
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.124799
obj = -1.531648, rho = -0.368174
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 327
nu = 0.102236
obj = -1.853614, rho = -0.448948
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*....*
optimization finished, #iter = 409
nu = 0.087672
obj = -2.257229, rho = -0.520627
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 311
nu = 0.073334
obj = -2.729308, rho = -0.577430
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 264
nu = 0.059749
obj = -3.361097, rho = -0.626482
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.054600
obj = -4.146958, rho = -0.954026
nSV = 8, nBSV = 3
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.048103
obj = -4.839089, rho = -1.023320
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.039182
obj = -5.525345, rho = -0.691343
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.030549
obj = -6.289153, rho = -0.511238
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.025779
obj = -7.082021, rho = -0.115826
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.020979
obj = -7.295184, rho = 0.264824
nSV = 6, nBSV = 0
Total nSV = 6
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.014584
obj = -7.295184, rho = 0.264824
nSV = 6, nBSV = 0
Total nSV = 6
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.586583
obj = -0.393749, rho = -0.200905
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.492223
obj = -0.474291, rho = -0.225022
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.407441
obj = -0.571925, rho = -0.272416
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.345727
obj = -0.691585, rho = -0.251835
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.289965
obj = -0.825880, rho = -0.225905
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.243574
obj = -0.982790, rho = -0.266793
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.200317
obj = -1.167311, rho = -0.269084
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.161007
obj = -1.403016, rho = -0.253838
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.132615
obj = -1.725290, rho = -0.270791
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.110641
obj = -2.159449, rho = -0.243033
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.102434
obj = -2.692070, rho = -0.299494
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.087073
obj = -3.263095, rho = -0.260812
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.071111
obj = -4.033335, rho = -0.295400
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.062856
obj = -5.057935, rho = -0.396318
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*...*
optimization finished, #iter = 649
nu = 0.057891
obj = -6.180408, rho = -0.463044
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*..*
optimization finished, #iter = 487
nu = 0.051459
obj = -7.081821, rho = -0.463713
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.043245
obj = -7.635701, rho = -0.390252
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.031753
obj = -7.675235, rho = -0.320409
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.022075
obj = -7.675235, rho = -0.320409
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.015346
obj = -7.675235, rho = -0.320409
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.644608
obj = -0.451308, rho = -0.037252
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.554377
obj = -0.554180, rho = -0.038565
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.479290
obj = -0.676789, rho = -0.042747
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.408293
obj = -0.814613, rho = -0.079185
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.335864
obj = -0.984019, rho = -0.056561
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.287694
obj = -1.199420, rho = 0.021683
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.240845
obj = -1.454129, rho = -0.062343
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.201282
obj = -1.778917, rho = -0.164379
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.172840
obj = -2.167183, rho = -0.193552
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.146033
obj = -2.621814, rho = -0.301532
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.118320
obj = -3.230480, rho = -0.327070
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.101742
obj = -4.056413, rho = -0.295380
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.091492
obj = -5.069704, rho = -0.286112
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.081221
obj = -6.209270, rho = 0.006882
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.070610
obj = -7.357522, rho = 0.445210
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.....*.*
optimization finished, #iter = 640
nu = 0.058554
obj = -8.544499, rho = 0.745439
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.048267
obj = -9.851568, rho = 0.908158
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.043606
obj = -10.539573, rho = 1.316338
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.030315
obj = -10.539573, rho = 1.316338
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.021075
obj = -10.539573, rho = 1.316338
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.605650
obj = -0.410470, rho = -0.155758
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.516477
obj = -0.495884, rho = -0.167838
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.434888
obj = -0.595957, rho = -0.007350
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.369931
obj = -0.708571, rho = 0.074240
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.301834
obj = -0.832190, rho = 0.039894
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.255010
obj = -0.968472, rho = -0.029060
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.200587
obj = -1.112478, rho = -0.015841
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.166111
obj = -1.278030, rho = 0.023783
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 284
nu = 0.129849
obj = -1.446404, rho = 0.012250
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.101448
obj = -1.663338, rho = -0.003630
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.084051
obj = -1.901573, rho = -0.145795
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.066717
obj = -2.134396, rho = -0.235671
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 245
nu = 0.050835
obj = -2.381281, rho = -0.254416
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.........*
optimization finished, #iter = 1221
nu = 0.042633
obj = -2.628282, rho = -0.162490
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*......*
optimization finished, #iter = 852
nu = 0.032554
obj = -2.738228, rho = -0.182743
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*....*
optimization finished, #iter = 527
nu = 0.023529
obj = -2.748114, rho = -0.171141
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*....*
optimization finished, #iter = 527
nu = 0.016357
obj = -2.748114, rho = -0.171141
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*....*
optimization finished, #iter = 527
nu = 0.011371
obj = -2.748114, rho = -0.171141
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*....*
optimization finished, #iter = 527
nu = 0.007905
obj = -2.748114, rho = -0.171141
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*....*
optimization finished, #iter = 527
nu = 0.005496
obj = -2.748114, rho = -0.171141
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.643855
obj = -0.434094, rho = -0.022972
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.543219
obj = -0.524121, rho = -0.022116
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.458142
obj = -0.625880, rho = 0.068569
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.382131
obj = -0.743937, rho = 0.116471
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.313274
obj = -0.881572, rho = 0.083721
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.261019
obj = -1.050635, rho = 0.106039
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.214961
obj = -1.241354, rho = 0.132705
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.178383
obj = -1.466844, rho = 0.142496
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.144351
obj = -1.726902, rho = 0.140011
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.120387
obj = -2.048827, rho = 0.132878
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.104589
obj = -2.352167, rho = 0.123966
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.080596
obj = -2.630042, rho = 0.152420
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
....*..*
optimization finished, #iter = 659
nu = 0.061024
obj = -3.007857, rho = 0.149064
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.051577
obj = -3.461753, rho = 0.114181
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.043763
obj = -3.725596, rho = 0.127351
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.032112
obj = -3.750282, rho = 0.327355
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.022324
obj = -3.750282, rho = 0.327355
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.015519
obj = -3.750282, rho = 0.327355
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.010789
obj = -3.750282, rho = 0.327355
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.007500
obj = -3.750282, rho = 0.327355
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.563398
obj = -0.369652, rho = 0.053465
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.476650
obj = -0.438191, rho = 0.038974
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.387815
obj = -0.510799, rho = -0.015749
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.321755
obj = -0.593637, rho = -0.133297
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.260634
obj = -0.679445, rho = -0.149995
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.206484
obj = -0.778658, rho = -0.161552
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.164916
obj = -0.887451, rho = -0.250862
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.134418
obj = -0.977775, rho = -0.401321
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.105793
obj = -1.064796, rho = -0.324264
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.080793
obj = -1.116290, rho = -0.316053
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.059006
obj = -1.144998, rho = -0.326766
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.042090
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.029260
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.020342
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.014141
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.009831
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.006834
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.004751
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.003303
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.002296
obj = -1.148072, rho = -0.386409
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.603910
obj = -0.419525, rho = -0.386432
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.520492
obj = -0.512282, rho = -0.396068
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.441897
obj = -0.622695, rho = -0.343189
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.373335
obj = -0.752307, rho = -0.473081
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.316430
obj = -0.912208, rho = -0.435457
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.265356
obj = -1.100452, rho = -0.451777
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.237013
obj = -1.293674, rho = -0.350622
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.190133
obj = -1.487338, rho = -0.381925
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.150077
obj = -1.691398, rho = -0.418043
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.119042
obj = -1.946252, rho = -0.344471
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*..*
optimization finished, #iter = 377
nu = 0.094146
obj = -2.262542, rho = -0.359523
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 290
nu = 0.076512
obj = -2.660088, rho = -0.378360
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
...*.*
optimization finished, #iter = 462
nu = 0.061253
obj = -3.129604, rho = -0.515506
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*..*
optimization finished, #iter = 534
nu = 0.052944
obj = -3.678061, rho = -0.678768
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.......*
optimization finished, #iter = 943
nu = 0.042153
obj = -4.160750, rho = -0.739046
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.......*
optimization finished, #iter = 771
nu = 0.032445
obj = -4.786709, rho = -0.685620
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.027868
obj = -5.545570, rho = -0.558248
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.023906
obj = -5.778103, rho = -0.423491
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.016620
obj = -5.778103, rho = -0.423491
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.011554
obj = -5.778103, rho = -0.423491
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.646832
obj = -0.443278, rho = -0.108912
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.540796
obj = -0.540512, rho = -0.145198
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.458878
obj = -0.664560, rho = -0.130408
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.387574
obj = -0.820720, rho = -0.140923
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.332719
obj = -1.014672, rho = -0.232588
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.287363
obj = -1.260086, rho = -0.201520
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.246909
obj = -1.564834, rho = -0.179192
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.217659
obj = -1.928910, rho = -0.244204
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.186698
obj = -2.370502, rho = -0.233003
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.157572
obj = -2.902238, rho = -0.262623
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.134012
obj = -3.575255, rho = -0.327585
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.116629
obj = -4.381623, rho = -0.469808
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 292
nu = 0.096979
obj = -5.363013, rho = -0.470038
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*..*
optimization finished, #iter = 416
nu = 0.084637
obj = -6.638811, rho = -0.239937
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 271
nu = 0.076385
obj = -7.968704, rho = 0.177659
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.....*...................*
optimization finished, #iter = 2434
nu = 0.062945
obj = -9.181134, rho = 0.419267
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 390
nu = 0.050331
obj = -10.721253, rho = 0.409308
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 344
nu = 0.044026
obj = -12.186816, rho = 0.408069
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*.*
optimization finished, #iter = 414
nu = 0.036289
obj = -12.613378, rho = 0.405956
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
...*.*
optimization finished, #iter = 414
nu = 0.025228
obj = -12.613378, rho = 0.405956
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.590839
obj = -0.400057, rho = -0.332545
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.504841
obj = -0.481718, rho = -0.281146
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.417195
obj = -0.578848, rho = -0.304618
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.355066
obj = -0.698035, rho = -0.283769
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.297609
obj = -0.831255, rho = -0.269977
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.246086
obj = -0.977400, rho = -0.302474
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.201548
obj = -1.153726, rho = -0.319030
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.172207
obj = -1.341398, rho = -0.319098
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.141320
obj = -1.503747, rho = -0.300806
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.109964
obj = -1.650639, rho = -0.458653
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.082821
obj = -1.805685, rho = -0.506299
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*.*
optimization finished, #iter = 428
nu = 0.063116
obj = -1.990370, rho = -0.508642
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.047342
obj = -2.216454, rho = -0.512598
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*...........*
optimization finished, #iter = 1386
nu = 0.035833
obj = -2.522638, rho = -0.486421
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.029653
obj = -2.924689, rho = -0.346210
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.026681
obj = -3.116622, rho = -0.091379
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.018548
obj = -3.116622, rho = -0.091379
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.012895
obj = -3.116622, rho = -0.091379
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.008964
obj = -3.116622, rho = -0.091379
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.006232
obj = -3.116622, rho = -0.091379
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.584105
obj = -0.386639, rho = -0.190437
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.482142
obj = -0.462598, rho = -0.223665
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.396695
obj = -0.554721, rho = -0.228671
nSV = 44, nBSV = 35
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.344030
obj = -0.665181, rho = -0.166336
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.283296
obj = -0.785806, rho = -0.128754
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.235033
obj = -0.926266, rho = -0.220778
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.192365
obj = -1.076257, rho = -0.219294
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.154192
obj = -1.265288, rho = -0.202481
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*....*
optimization finished, #iter = 424
nu = 0.129095
obj = -1.471181, rho = -0.129402
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.102574
obj = -1.699453, rho = -0.084254
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.085600
obj = -1.952309, rho = -0.077731
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.068646
obj = -2.177002, rho = -0.096325
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.056888
obj = -2.351255, rho = -0.323760
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.042019
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.029211
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.020307
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.014118
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.009814
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.006823
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.004743
obj = -2.371474, rho = -0.389893
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.612250
obj = -0.423831, rho = -0.098375
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.520280
obj = -0.518850, rho = -0.111750
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.444464
obj = -0.635030, rho = -0.085547
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.378247
obj = -0.776363, rho = -0.017670
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.323431
obj = -0.947297, rho = 0.004896
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.270963
obj = -1.157245, rho = 0.018346
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.232784
obj = -1.414343, rho = -0.001274
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.197041
obj = -1.710396, rho = -0.004574
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.165658
obj = -2.085581, rho = -0.025979
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 251
nu = 0.136626
obj = -2.547783, rho = -0.029947
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.118126
obj = -3.144254, rho = -0.023505
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.101049
obj = -3.885220, rho = 0.118555
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.092508
obj = -4.733789, rho = 0.352433
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 264
nu = 0.079542
obj = -5.429590, rho = 0.535287
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.063380
obj = -6.210435, rho = 0.703446
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 394
nu = 0.056552
obj = -6.655605, rho = 1.004870
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*..*
optimization finished, #iter = 501
nu = 0.039625
obj = -6.656724, rho = 1.015336
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*..*
optimization finished, #iter = 501
nu = 0.027547
obj = -6.656724, rho = 1.015336
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*..*
optimization finished, #iter = 501
nu = 0.019151
obj = -6.656724, rho = 1.015336
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*..*
optimization finished, #iter = 501
nu = 0.013313
obj = -6.656724, rho = 1.015336
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.536131
obj = -0.357587, rho = -0.170726
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.449542
obj = -0.427816, rho = -0.130097
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.382655
obj = -0.506384, rho = -0.121674
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.310759
obj = -0.591427, rho = -0.202116
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.255432
obj = -0.688285, rho = -0.210718
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.207094
obj = -0.793962, rho = -0.108008
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.170176
obj = -0.912739, rho = -0.088785
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.138436
obj = -1.021717, rho = -0.148859
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 245
nu = 0.105362
obj = -1.123264, rho = -0.125575
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*..*
optimization finished, #iter = 246
nu = 0.080653
obj = -1.251039, rho = -0.100311
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.062287
obj = -1.402373, rho = -0.069253
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.048020
obj = -1.577687, rho = -0.108055
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 335
nu = 0.038911
obj = -1.764640, rho = -0.070978
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.029954
obj = -1.946809, rho = -0.105205
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.025212
obj = -2.047505, rho = -0.073040
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.017527
obj = -2.047505, rho = -0.073040
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.012185
obj = -2.047505, rho = -0.073040
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.008471
obj = -2.047505, rho = -0.073040
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.005889
obj = -2.047505, rho = -0.073040
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.004094
obj = -2.047505, rho = -0.073040
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.594408
obj = -0.409913, rho = -0.148663
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.500981
obj = -0.504018, rho = -0.153539
nSV = 52, nBSV = 50
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.436905
obj = -0.611662, rho = -0.176457
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.370611
obj = -0.740906, rho = -0.198669
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.311290
obj = -0.891108, rho = -0.157564
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.260958
obj = -1.059480, rho = -0.157378
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.215709
obj = -1.259907, rho = -0.161892
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.175317
obj = -1.520070, rho = -0.180598
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.154815
obj = -1.829438, rho = -0.197720
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.133179
obj = -2.085751, rho = -0.231737
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 280
nu = 0.101826
obj = -2.352786, rho = -0.264854
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*.*
optimization finished, #iter = 322
nu = 0.080782
obj = -2.682597, rho = -0.276651
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*...*
optimization finished, #iter = 628
nu = 0.063832
obj = -3.058929, rho = -0.297021
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*...*
optimization finished, #iter = 433
nu = 0.048686
obj = -3.544441, rho = -0.292908
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.039646
obj = -4.219386, rho = -0.362386
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.033227
obj = -4.947377, rho = -0.513323
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.028006
obj = -5.819128, rho = -0.594687
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.025814
obj = -6.239222, rho = -0.940040
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.017946
obj = -6.239222, rho = -0.940040
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.012476
obj = -6.239222, rho = -0.940040
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.586620
obj = -0.407038, rho = -0.133815
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.495289
obj = -0.501327, rho = -0.147535
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.432480
obj = -0.616736, rho = -0.141187
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.376465
obj = -0.745386, rho = -0.113832
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.312667
obj = -0.891162, rho = -0.073513
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.265241
obj = -1.059218, rho = -0.046830
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.217892
obj = -1.252374, rho = -0.093336
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.193172
obj = -1.445400, rho = -0.181697
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.149691
obj = -1.604518, rho = -0.231660
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.117531
obj = -1.774322, rho = -0.185857
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.090744
obj = -1.948375, rho = -0.193379
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.073057
obj = -2.073713, rho = -0.077876
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 425
nu = 0.053077
obj = -2.129363, rho = -0.103949
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.037831
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.026300
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.018283
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.012711
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.008836
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.006143
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.004271
obj = -2.135091, rho = -0.100184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.622594
obj = -0.415862, rho = -0.210118
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.514416
obj = -0.502656, rho = -0.240831
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.440735
obj = -0.600952, rho = -0.224402
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.364236
obj = -0.716671, rho = -0.225931
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.300781
obj = -0.860656, rho = -0.175976
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.248441
obj = -1.038457, rho = -0.145281
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.207962
obj = -1.252990, rho = -0.133478
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.172711
obj = -1.525572, rho = -0.269688
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.141429
obj = -1.891222, rho = -0.253209
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.125742
obj = -2.377259, rho = -0.160379
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.106522
obj = -2.977574, rho = -0.241012
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.093609
obj = -3.778263, rho = -0.174863
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.081063
obj = -4.795602, rho = -0.082656
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.072488
obj = -6.104904, rho = -0.016612
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.065147
obj = -7.732487, rho = -0.233892
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.057831
obj = -9.660372, rho = -0.356943
nSV = 8, nBSV = 3
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.052797
obj = -11.821240, rho = -0.073032
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.046200
obj = -13.896690, rho = -0.423878
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.040872
obj = -15.485278, rho = -1.132497
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 361
nu = 0.031340
obj = -15.673285, rho = -1.473307
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.579979
obj = -0.412066, rho = -0.175729
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 95% (95/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.506438
obj = -0.512085, rho = -0.101682
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.444502
obj = -0.627954, rho = -0.080124
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.374581
obj = -0.766360, rho = -0.054915
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.316526
obj = -0.932596, rho = -0.026237
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.273182
obj = -1.129353, rho = -0.188701
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.231828
obj = -1.352279, rho = -0.156925
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.186375
obj = -1.622546, rho = -0.133090
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.155213
obj = -1.990227, rho = -0.129591
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.132709
obj = -2.443597, rho = -0.137539
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.115176
obj = -3.009201, rho = -0.090285
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.105151
obj = -3.580454, rho = 0.139008
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.087224
obj = -4.056563, rho = 0.284494
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.068643
obj = -4.516511, rho = 0.245207
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.052885
obj = -5.032207, rho = 0.343593
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.043728
obj = -5.544337, rho = 0.251084
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.033394
obj = -5.609939, rho = 0.193893
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.023215
obj = -5.609939, rho = 0.193893
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.016139
obj = -5.609939, rho = 0.193893
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.011220
obj = -5.609939, rho = 0.193893
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.615181
obj = -0.433592, rho = -0.274533
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.536713
obj = -0.533739, rho = -0.325057
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.460967
obj = -0.651160, rho = -0.372165
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.387354
obj = -0.793350, rho = -0.302535
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.331780
obj = -0.964526, rho = -0.332166
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.279947
obj = -1.167272, rho = -0.384782
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.243697
obj = -1.401069, rho = -0.272855
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.202107
obj = -1.639826, rho = -0.234676
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.163723
obj = -1.926575, rho = -0.254158
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.132262
obj = -2.274136, rho = -0.265295
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.111902
obj = -2.668497, rho = -0.400301
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.096039
obj = -3.012187, rho = -0.771509
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.073069
obj = -3.296030, rho = -0.832923
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*...*
optimization finished, #iter = 397
nu = 0.055927
obj = -3.614509, rho = -0.758788
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*...*
optimization finished, #iter = 439
nu = 0.042679
obj = -3.967591, rho = -0.689280
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*........*
optimization finished, #iter = 991
nu = 0.032922
obj = -4.342899, rho = -0.650863
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.026993
obj = -4.534346, rho = -1.001294
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.018765
obj = -4.534346, rho = -1.001294
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.013045
obj = -4.534346, rho = -1.001294
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.009069
obj = -4.534346, rho = -1.001294
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.616785
obj = -0.415735, rho = -0.020572
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.520000
obj = -0.501319, rho = -0.001669
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.434640
obj = -0.603675, rho = -0.011981
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.361529
obj = -0.725970, rho = 0.004150
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.302551
obj = -0.878388, rho = 0.012427
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.255796
obj = -1.057609, rho = 0.003246
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.220646
obj = -1.254757, rho = 0.009943
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.179266
obj = -1.467270, rho = -0.043551
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*..*
optimization finished, #iter = 473
nu = 0.144953
obj = -1.729804, rho = -0.018464
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*....*
optimization finished, #iter = 406
nu = 0.116824
obj = -2.068567, rho = -0.020190
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 294
nu = 0.097452
obj = -2.486755, rho = -0.023251
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.081649
obj = -2.972458, rho = 0.038620
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.068171
obj = -3.579877, rho = 0.224495
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.058259
obj = -4.271682, rho = 0.548901
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 311
nu = 0.048934
obj = -5.023217, rho = 0.669841
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.038773
obj = -5.897875, rho = 0.686743
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.032182
obj = -7.017129, rho = 0.655032
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.026413
obj = -8.374598, rho = 0.929927
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.023312
obj = -9.688236, rho = 1.233023
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
...*.*
optimization finished, #iter = 497
nu = 0.020610
obj = -10.307039, rho = 1.239158
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.598941
obj = -0.380819, rho = -0.037344
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.482853
obj = -0.443263, rho = -0.047391
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.394887
obj = -0.517016, rho = -0.035733
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.320151
obj = -0.602768, rho = -0.029687
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.257297
obj = -0.705431, rho = -0.052993
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.209610
obj = -0.830070, rho = -0.030872
nSV = 23, nBSV = 19
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.172885
obj = -0.967440, rho = -0.019664
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.140264
obj = -1.126135, rho = -0.050778
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.116566
obj = -1.298429, rho = -0.091033
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 170
nu = 0.097024
obj = -1.433203, rho = -0.107932
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.073635
obj = -1.542068, rho = -0.123678
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.056476
obj = -1.632830, rho = -0.060489
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.042092
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.029262
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.020343
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.014142
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.009832
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.006835
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.004752
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.003303
obj = -1.651396, rho = 0.018855
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.568694
obj = -0.383780, rho = -0.214226
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.480000
obj = -0.462445, rho = -0.234060
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.402261
obj = -0.556445, rho = -0.297487
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.340000
obj = -0.668937, rho = -0.268622
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.286276
obj = -0.790155, rho = -0.255805
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.232016
obj = -0.928064, rho = -0.293474
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.190506
obj = -1.100135, rho = -0.273198
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.158054
obj = -1.300321, rho = -0.289723
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.127800
obj = -1.539042, rho = -0.222213
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.104086
obj = -1.843584, rho = -0.335233
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.085600
obj = -2.239328, rho = -0.436961
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.071545
obj = -2.743487, rho = -0.589309
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.059878
obj = -3.416924, rho = -0.672643
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.052513
obj = -4.286001, rho = -0.854323
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.047773
obj = -5.298376, rho = -1.099229
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.041984
obj = -6.321591, rho = -0.940251
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.037049
obj = -7.276833, rho = -0.924346
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.031449
obj = -7.601086, rho = -1.094281
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.021863
obj = -7.601086, rho = -1.094281
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.015199
obj = -7.601086, rho = -1.094281
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.563373
obj = -0.386902, rho = -0.145227
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.486676
obj = -0.471225, rho = -0.125045
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.400190
obj = -0.570798, rho = -0.129346
nSV = 42, nBSV = 40
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.342633
obj = -0.694272, rho = -0.142748
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.297362
obj = -0.837610, rho = -0.247681
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.250360
obj = -0.991370, rho = -0.127785
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 177
nu = 0.202060
obj = -1.175460, rho = -0.176909
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.168239
obj = -1.396670, rho = -0.100989
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 243
nu = 0.133554
obj = -1.675578, rho = -0.103873
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.111222
obj = -2.066925, rho = -0.069453
nSV = 13, nBSV = 9
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.099584
obj = -2.500234, rho = 0.038096
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.081092
obj = -3.005711, rho = 0.051497
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.067850
obj = -3.640992, rho = 0.063272
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.059406
obj = -4.436034, rho = -0.028059
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.053492
obj = -5.162621, rho = -0.017090
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.043463
obj = -5.655493, rho = -0.016515
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.033986
obj = -5.868917, rho = 0.010323
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.024364
obj = -5.888431, rho = -0.034135
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.016938
obj = -5.888431, rho = -0.034135
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.011775
obj = -5.888431, rho = -0.034135
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.564584
obj = -0.384831, rho = -0.192897
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.477122
obj = -0.467073, rho = -0.238441
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.403510
obj = -0.566207, rho = -0.209816
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.343393
obj = -0.680184, rho = -0.196790
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.288496
obj = -0.815304, rho = -0.139242
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.239808
obj = -0.971735, rho = -0.134650
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.195986
obj = -1.163096, rho = -0.077451
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.165602
obj = -1.391090, rho = 0.019927
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.137212
obj = -1.650490, rho = 0.055429
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.110826
obj = -1.988293, rho = 0.092511
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.092876
obj = -2.418046, rho = 0.190369
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.**..*
optimization finished, #iter = 339
nu = 0.076815
obj = -2.966869, rho = 0.216810
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.066062
obj = -3.700695, rho = 0.186744
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.060258
obj = -4.487783, rho = 0.379381
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.049959
obj = -5.338342, rho = 0.335389
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.042548
obj = -6.380651, rho = 0.325090
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.038287
obj = -7.150069, rho = 0.509080
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.030086
obj = -7.271851, rho = 0.596926
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.020916
obj = -7.271851, rho = 0.596926
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.014540
obj = -7.271851, rho = 0.596926
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.609104
obj = -0.435337, rho = -0.213385
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.534028
obj = -0.539366, rho = -0.225208
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.462025
obj = -0.662004, rho = -0.252225
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.394132
obj = -0.814139, rho = -0.203027
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.339449
obj = -0.996089, rho = -0.160335
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.291504
obj = -1.207730, rho = -0.052784
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.245055
obj = -1.460259, rho = -0.050247
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.210095
obj = -1.752146, rho = 0.054428
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.180130
obj = -2.045436, rho = 0.126456
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.144019
obj = -2.363713, rho = 0.166135
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.118167
obj = -2.696123, rho = 0.274596
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.097369
obj = -3.030143, rho = 0.205498
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.075103
obj = -3.269320, rho = 0.147628
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.057659
obj = -3.468366, rho = 0.040490
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 253
nu = 0.042276
obj = -3.601647, rho = -0.019946
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.031197
obj = -3.643349, rho = -0.113686
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.021688
obj = -3.643349, rho = -0.113686
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.015077
obj = -3.643349, rho = -0.113686
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.010481
obj = -3.643349, rho = -0.113686
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.007287
obj = -3.643349, rho = -0.113686
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.559941
obj = -0.384750, rho = 0.063778
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.493014
obj = -0.463602, rho = 0.152174
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.403181
obj = -0.550280, rho = 0.140280
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.340000
obj = -0.655524, rho = 0.070736
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.278044
obj = -0.770594, rho = 0.003491
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.228290
obj = -0.904361, rho = 0.007449
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.198846
obj = -1.041766, rho = 0.101045
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.154800
obj = -1.162088, rho = 0.167088
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.119380
obj = -1.304824, rho = 0.171211
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.094544
obj = -1.459194, rho = 0.204767
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.072791
obj = -1.629832, rho = 0.229176
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.057035
obj = -1.833272, rho = 0.287072
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.043775
obj = -2.059910, rho = 0.314302
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.037529
obj = -2.253104, rho = 0.150938
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.027947
obj = -2.269429, rho = 0.043123
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.019429
obj = -2.269429, rho = 0.043123
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.013507
obj = -2.269429, rho = 0.043123
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.009390
obj = -2.269429, rho = 0.043123
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.006528
obj = -2.269429, rho = 0.043123
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.004538
obj = -2.269429, rho = 0.043123
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.629928
obj = -0.440823, rho = -0.204817
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.548885
obj = -0.539596, rho = -0.266590
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.471090
obj = -0.655681, rho = -0.303237
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.397896
obj = -0.784244, rho = -0.217258
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.332089
obj = -0.936829, rho = -0.307796
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.269928
obj = -1.125769, rho = -0.300736
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.221028
obj = -1.375874, rho = -0.345870
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.185235
obj = -1.715497, rho = -0.389095
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.164101
obj = -2.138983, rho = -0.567756
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 287
nu = 0.143032
obj = -2.639150, rho = -0.611666
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.122018
obj = -3.251691, rho = -0.681079
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.102197
obj = -4.007923, rho = -0.694319
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.093778
obj = -4.947409, rho = -1.044861
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
...*..*
optimization finished, #iter = 581
nu = 0.085872
obj = -5.754886, rho = -1.258216
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*...*
optimization finished, #iter = 559
nu = 0.069576
obj = -6.393094, rho = -1.608368
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..........**.*
optimization finished, #iter = 1097
nu = 0.055119
obj = -6.747382, rho = -1.627185
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
.....*
optimization finished, #iter = 576
nu = 0.041126
obj = -6.909793, rho = -1.641154
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
.....*
optimization finished, #iter = 576
nu = 0.028590
obj = -6.909793, rho = -1.641154
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
.....*
optimization finished, #iter = 576
nu = 0.019876
obj = -6.909793, rho = -1.641154
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
.....*
optimization finished, #iter = 576
nu = 0.013818
obj = -6.909793, rho = -1.641154
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.542887
obj = -0.369776, rho = -0.213619
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.463226
obj = -0.446866, rho = -0.121906
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.389743
obj = -0.537593, rho = -0.185814
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.324778
obj = -0.645211, rho = -0.211435
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.267432
obj = -0.778484, rho = -0.243086
nSV = 32, nBSV = 22
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.233500
obj = -0.941346, rho = -0.126793
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.195028
obj = -1.097712, rho = -0.104927
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.157209
obj = -1.289578, rho = -0.166931
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.127599
obj = -1.518816, rho = -0.227776
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.103346
obj = -1.804346, rho = -0.271353
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.088548
obj = -2.133595, rho = -0.292830
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 268
nu = 0.069625
obj = -2.520121, rho = -0.311963
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*..*
optimization finished, #iter = 523
nu = 0.056651
obj = -3.044181, rho = -0.355297
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*....*
optimization finished, #iter = 506
nu = 0.049270
obj = -3.658547, rho = -0.376277
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 373
nu = 0.042266
obj = -4.309958, rho = -0.412011
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.037264
obj = -4.883635, rho = -0.701518
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
....*
optimization finished, #iter = 487
nu = 0.029761
obj = -4.999626, rho = -0.889354
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
....*
optimization finished, #iter = 487
nu = 0.020690
obj = -4.999626, rho = -0.889354
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
....*
optimization finished, #iter = 487
nu = 0.014383
obj = -4.999626, rho = -0.889354
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
....*
optimization finished, #iter = 487
nu = 0.009999
obj = -4.999626, rho = -0.889354
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.570633
obj = -0.384205, rho = -0.246089
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.479606
obj = -0.461275, rho = -0.205866
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.395872
obj = -0.556139, rho = -0.165155
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.330855
obj = -0.676106, rho = -0.168049
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.280000
obj = -0.823138, rho = -0.108812
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.238183
obj = -1.000915, rho = -0.020589
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.200205
obj = -1.214137, rho = 0.027338
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 280
nu = 0.177857
obj = -1.447601, rho = 0.121617
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.140282
obj = -1.711774, rho = 0.082399
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.113166
obj = -2.076119, rho = 0.046237
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.098242
obj = -2.536256, rho = 0.016091
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.084079
obj = -3.023039, rho = 0.068105
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.071214
obj = -3.595775, rho = -0.143166
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 341
nu = 0.061038
obj = -4.134734, rho = -0.518754
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.049729
obj = -4.589819, rho = -0.708106
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*....*
optimization finished, #iter = 507
nu = 0.040069
obj = -4.881211, rho = -0.816830
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
....*
optimization finished, #iter = 481
nu = 0.029305
obj = -4.923778, rho = -0.745492
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*
optimization finished, #iter = 481
nu = 0.020373
obj = -4.923778, rho = -0.745492
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*
optimization finished, #iter = 481
nu = 0.014163
obj = -4.923778, rho = -0.745492
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*
optimization finished, #iter = 481
nu = 0.009846
obj = -4.923778, rho = -0.745492
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.631289
obj = -0.422440, rho = -0.262493
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.539885
obj = -0.503110, rho = -0.230793
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.436194
obj = -0.597882, rho = -0.214811
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.365108
obj = -0.714370, rho = -0.195523
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.304778
obj = -0.846500, rho = -0.210946
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.251897
obj = -0.995460, rho = -0.271236
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.209427
obj = -1.159516, rho = -0.209639
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.172918
obj = -1.317901, rho = -0.216845
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.137988
obj = -1.490409, rho = -0.222491
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.110162
obj = -1.648483, rho = -0.293429
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.085157
obj = -1.793450, rho = -0.299553
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.064621
obj = -1.914774, rho = -0.253423
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.049738
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.034578
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.024038
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.016711
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.011617
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.008076
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.005615
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.003903
obj = -1.951920, rho = -0.222463
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.564462
obj = -0.386398, rho = -0.276209
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.481412
obj = -0.468147, rho = -0.274929
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.404945
obj = -0.567547, rho = -0.258633
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.344200
obj = -0.682512, rho = -0.328750
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.294710
obj = -0.808164, rho = -0.240022
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.241344
obj = -0.944441, rho = -0.161695
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.199935
obj = -1.094529, rho = -0.172552
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.157039
obj = -1.257397, rho = -0.233457
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.126645
obj = -1.463880, rho = -0.261196
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.105991
obj = -1.692114, rho = -0.452659
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.082945
obj = -1.922752, rho = -0.522513
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.066165
obj = -2.214466, rho = -0.602955
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.057005
obj = -2.440249, rho = -0.771548
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.043892
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.030514
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.021213
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.014747
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.010252
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.007127
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.004955
obj = -2.477534, rho = -0.761245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.626987
obj = -0.423057, rho = -0.023673
nSV = 67, nBSV = 60
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.531549
obj = -0.511278, rho = -0.044607
nSV = 54, nBSV = 52
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.449539
obj = -0.612164, rho = -0.090513
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.381829
obj = -0.720855, rho = -0.018258
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.304933
obj = -0.846129, rho = 0.003194
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.250454
obj = -0.994244, rho = -0.089059
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.204244
obj = -1.177322, rho = -0.107900
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.169998
obj = -1.377312, rho = -0.153296
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.135038
obj = -1.611263, rho = -0.116937
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.114056
obj = -1.909917, rho = -0.115372
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.094348
obj = -2.204623, rho = -0.254802
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.078068
obj = -2.476010, rho = -0.334168
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.....*
optimization finished, #iter = 646
nu = 0.061910
obj = -2.684964, rho = -0.184341
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.047411
obj = -2.862502, rho = -0.072529
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.035840
obj = -2.909679, rho = 0.041516
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.024915
obj = -2.909679, rho = 0.041516
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.017321
obj = -2.909679, rho = 0.041516
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.012041
obj = -2.909679, rho = 0.041516
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.008371
obj = -2.909679, rho = 0.041516
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.005820
obj = -2.909679, rho = 0.041516
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.607132
obj = -0.396856, rho = -0.130432
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.500104
obj = -0.469482, rho = -0.146569
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.411676
obj = -0.559515, rho = -0.141011
nSV = 42, nBSV = 39
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.339241
obj = -0.666367, rho = -0.157489
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.293711
obj = -0.792439, rho = -0.169282
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.243595
obj = -0.908934, rho = -0.087941
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.193227
obj = -1.032992, rho = -0.054924
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.150951
obj = -1.168216, rho = -0.086631
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
...*..*
optimization finished, #iter = 540
nu = 0.118770
obj = -1.318553, rho = -0.070186
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 170
nu = 0.092967
obj = -1.499485, rho = -0.009738
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.072369
obj = -1.729932, rho = 0.017463
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.060785
obj = -2.007957, rho = -0.023112
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.047638
obj = -2.273262, rho = -0.086810
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 170
nu = 0.037959
obj = -2.587871, rho = -0.122569
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.032528
obj = -2.847225, rho = -0.231913
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.024633
obj = -2.876883, rho = -0.282247
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.017125
obj = -2.876883, rho = -0.282247
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.011905
obj = -2.876883, rho = -0.282247
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.008276
obj = -2.876883, rho = -0.282247
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.005754
obj = -2.876883, rho = -0.282247
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.577893
obj = -0.404220, rho = 0.062147
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.494381
obj = -0.499583, rho = 0.049619
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.430040
obj = -0.610641, rho = 0.077824
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.356417
obj = -0.747700, rho = 0.047683
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.302351
obj = -0.927938, rho = 0.031741
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.269547
obj = -1.141591, rho = 0.236839
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.225046
obj = -1.405313, rho = 0.243372
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.192667
obj = -1.741036, rho = 0.182075
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.164422
obj = -2.158156, rho = 0.218940
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.140643
obj = -2.698748, rho = 0.087198
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.119314
obj = -3.416453, rho = 0.143962
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.103849
obj = -4.404514, rho = 0.158580
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.098374
obj = -5.653957, rho = 0.410059
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 97% (97/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.084057
obj = -7.192337, rho = 0.396225
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 97% (97/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.078138
obj = -9.115561, rho = 0.165191
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.073509
obj = -11.061302, rho = -0.086599
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.065415
obj = -12.439530, rho = -0.585012
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.051793
obj = -13.428277, rho = -0.932627
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
..*..*
optimization finished, #iter = 441
nu = 0.038961
obj = -13.545737, rho = -1.070896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
..*..*
optimization finished, #iter = 441
nu = 0.027085
obj = -13.545737, rho = -1.070896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.576404
obj = -0.396179, rho = -0.046816
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.495638
obj = -0.482417, rho = 0.021851
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.421047
obj = -0.580237, rho = 0.093593
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.351802
obj = -0.692002, rho = 0.098912
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.294927
obj = -0.824225, rho = 0.128478
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.243484
obj = -0.979302, rho = 0.113667
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.200899
obj = -1.165481, rho = 0.045926
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.165176
obj = -1.392052, rho = 0.015498
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 347
nu = 0.134973
obj = -1.662585, rho = 0.009676
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*......*
optimization finished, #iter = 660
nu = 0.109275
obj = -2.032689, rho = -0.005736
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.091965
obj = -2.534102, rho = -0.104027
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.081015
obj = -3.200909, rho = -0.152040
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.073005
obj = -3.939847, rho = -0.310316
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.061523
obj = -4.778799, rho = -0.401597
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.058695
obj = -5.615295, rho = -0.593275
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.049958
obj = -5.925612, rho = -0.822250
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.035290
obj = -5.928006, rho = -0.866629
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.024534
obj = -5.928006, rho = -0.866629
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.017056
obj = -5.928006, rho = -0.866629
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.011857
obj = -5.928006, rho = -0.866629
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.595593
obj = -0.415601, rho = -0.130242
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.516963
obj = -0.512039, rho = -0.106690
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.438037
obj = -0.628574, rho = -0.065828
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.381297
obj = -0.757268, rho = 0.035327
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.321014
obj = -0.903582, rho = 0.016909
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.264992
obj = -1.070808, rho = -0.019623
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.217292
obj = -1.279114, rho = -0.056729
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.175200
obj = -1.549620, rho = -0.058168
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.147389
obj = -1.904347, rho = -0.100926
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.130454
obj = -2.327011, rho = -0.141612
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 105
nu = 0.109162
obj = -2.840068, rho = -0.115151
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.097152
obj = -3.418124, rho = -0.022803
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.081910
obj = -3.973601, rho = -0.118878
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.068405
obj = -4.495496, rho = 0.086862
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.054865
obj = -4.952843, rho = 0.118907
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.041631
obj = -5.263175, rho = 0.169945
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.032232
obj = -5.414912, rho = 0.207110
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.022407
obj = -5.414912, rho = 0.207110
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.015577
obj = -5.414912, rho = 0.207110
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.010829
obj = -5.414912, rho = 0.207110
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.601252
obj = -0.432091, rho = -0.120366
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.518415
obj = -0.540873, rho = -0.168305
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.445861
obj = -0.682030, rho = -0.156072
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.404481
obj = -0.862218, rho = -0.058434
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.355770
obj = -1.070340, rho = -0.020251
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.301865
obj = -1.324971, rho = 0.004853
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.258395
obj = -1.650333, rho = 0.103520
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.222822
obj = -2.064498, rho = 0.160371
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.190941
obj = -2.610902, rho = 0.105667
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.167082
obj = -3.336050, rho = 0.193558
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.145078
obj = -4.301799, rho = 0.247512
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.129592
obj = -5.633956, rho = 0.272463
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.120615
obj = -7.332598, rho = 0.381520
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 388
nu = 0.109190
obj = -9.415444, rho = 0.391374
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.098268
obj = -12.162437, rho = 0.333564
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 420
nu = 0.086612
obj = -15.587029, rho = 0.398480
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*.*
optimization finished, #iter = 410
nu = 0.076219
obj = -20.250014, rho = 0.500251
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..........*
optimization finished, #iter = 1200
nu = 0.067749
obj = -26.737024, rho = 0.550517
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*.*
optimization finished, #iter = 417
nu = 0.063983
obj = -35.350759, rho = 0.760930
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
....*.*
optimization finished, #iter = 545
nu = 0.061246
obj = -45.546664, rho = 1.265298
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.648020
obj = -0.448282, rho = -0.069456
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.549606
obj = -0.546314, rho = -0.106468
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.470761
obj = -0.668285, rho = -0.092374
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.404736
obj = -0.813383, rho = -0.020619
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.353344
obj = -0.973513, rho = -0.116771
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.291350
obj = -1.139834, rho = -0.151105
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.235561
obj = -1.328122, rho = -0.156442
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.193625
obj = -1.554891, rho = -0.278948
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*...*
optimization finished, #iter = 583
nu = 0.155358
obj = -1.804732, rho = -0.367492
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*
optimization finished, #iter = 299
nu = 0.122925
obj = -2.123841, rho = -0.418197
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 245
nu = 0.100535
obj = -2.540050, rho = -0.526733
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*....*
optimization finished, #iter = 591
nu = 0.087645
obj = -2.986207, rho = -0.684786
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.071975
obj = -3.415777, rho = -0.673830
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 281
nu = 0.059040
obj = -3.807729, rho = -0.702686
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
......*...*
optimization finished, #iter = 989
nu = 0.045638
obj = -4.177737, rho = -0.732278
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.036010
obj = -4.490624, rho = -0.888993
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.026942
obj = -4.526408, rho = -0.961999
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.018730
obj = -4.526408, rho = -0.961999
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.013021
obj = -4.526408, rho = -0.961999
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.009052
obj = -4.526408, rho = -0.961999
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.620804
obj = -0.429265, rho = -0.265752
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.532506
obj = -0.523152, rho = -0.242406
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.455055
obj = -0.634828, rho = -0.192342
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.380000
obj = -0.772894, rho = -0.169274
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.316043
obj = -0.942068, rho = -0.177461
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.273130
obj = -1.158956, rho = -0.195342
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.241438
obj = -1.401279, rho = -0.321676
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.200300
obj = -1.647279, rho = -0.367806
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.167618
obj = -1.928475, rho = -0.453914
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.133854
obj = -2.259019, rho = -0.358300
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.109537
obj = -2.651400, rho = -0.294611
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.092552
obj = -3.016844, rho = -0.486043
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.073100
obj = -3.387430, rho = -0.644633
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.059287
obj = -3.717757, rho = -0.552450
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.047506
obj = -3.857324, rho = -0.544522
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.033025
obj = -3.857324, rho = -0.544875
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.022959
obj = -3.857324, rho = -0.544875
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.015961
obj = -3.857324, rho = -0.544875
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.011096
obj = -3.857324, rho = -0.544875
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.007714
obj = -3.857324, rho = -0.544875
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.591587
obj = -0.391451, rho = -0.008620
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.495861
obj = -0.468403, rho = 0.015877
nSV = 50, nBSV = 47
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.405901
obj = -0.559107, rho = -0.003632
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.336448
obj = -0.674181, rho = -0.060148
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.286260
obj = -0.811504, rho = -0.170819
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.233384
obj = -0.973360, rho = -0.166949
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.192844
obj = -1.181147, rho = -0.215605
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.166033
obj = -1.444332, rho = -0.155653
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.147761
obj = -1.721042, rho = -0.043832
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.124147
obj = -1.974306, rho = 0.057103
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.096669
obj = -2.237984, rho = 0.090479
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 382
nu = 0.077376
obj = -2.537626, rho = 0.143027
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.060809
obj = -2.860987, rho = 0.191445
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.047813
obj = -3.215335, rho = 0.111524
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.038636
obj = -3.576968, rho = 0.074374
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.032130
obj = -3.753155, rho = 0.040869
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.022336
obj = -3.753155, rho = 0.040869
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.015528
obj = -3.753155, rho = 0.040869
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.010795
obj = -3.753155, rho = 0.040869
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.007505
obj = -3.753155, rho = 0.040869
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.680000
obj = -0.471488, rho = -0.184057
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.584049
obj = -0.575992, rho = -0.220450
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.498177
obj = -0.698423, rho = -0.178446
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.417066
obj = -0.845208, rho = -0.284967
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.352948
obj = -1.028212, rho = -0.357017
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.295684
obj = -1.243137, rho = -0.371887
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.249665
obj = -1.511073, rho = -0.511352
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.205233
obj = -1.857875, rho = -0.531440
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.174158
obj = -2.324323, rho = -0.502005
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.146931
obj = -2.946235, rho = -0.528072
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.130403
obj = -3.785306, rho = -0.583162
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.119259
obj = -4.850391, rho = -0.719052
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.104939
obj = -6.160343, rho = -0.967798
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.094205
obj = -7.804205, rho = -1.305384
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.083638
obj = -9.857838, rho = -1.571561
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.078132
obj = -12.034491, rho = -2.003366
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.....*
optimization finished, #iter = 655
nu = 0.071241
obj = -13.852062, rho = -2.444792
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
....*..*
optimization finished, #iter = 665
nu = 0.058871
obj = -14.701265, rho = -3.138302
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.....*.*
optimization finished, #iter = 680
nu = 0.042652
obj = -14.828111, rho = -3.337827
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
.....*.*
optimization finished, #iter = 680
nu = 0.029651
obj = -14.828111, rho = -3.337827
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.603203
obj = -0.424514, rho = 0.027661
nSV = 64, nBSV = 57
Total nSV = 64
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.515627
obj = -0.525016, rho = 0.035997
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.442393
obj = -0.648853, rho = -0.004552
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.389789
obj = -0.793357, rho = -0.069343
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.331205
obj = -0.963916, rho = -0.069411
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.274866
obj = -1.179453, rho = -0.075862
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.234547
obj = -1.455704, rho = 0.010723
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.195498
obj = -1.803691, rho = 0.032454
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.166699
obj = -2.280586, rho = 0.072016
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.145300
obj = -2.906098, rho = 0.039285
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.131067
obj = -3.693644, rho = -0.086128
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.110828
obj = -4.749503, rho = -0.104990
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.097772
obj = -6.224523, rho = -0.168661
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.091377
obj = -8.208023, rho = -0.247282
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.087499
obj = -10.619824, rho = -0.153871
nSV = 11, nBSV = 6
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.080594
obj = -13.302190, rho = -0.033941
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.072061
obj = -16.267622, rho = 0.226055
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.065227
obj = -19.113338, rho = 0.705503
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 317
nu = 0.058237
obj = -20.790273, rho = 0.867599
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
....*.*
optimization finished, #iter = 552
nu = 0.041609
obj = -20.809726, rho = 0.870089
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.604754
obj = -0.406923, rho = -0.026266
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.505491
obj = -0.489000, rho = -0.005419
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.431162
obj = -0.590917, rho = -0.056588
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.364875
obj = -0.699611, rho = 0.055042
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.300396
obj = -0.819175, rho = 0.091084
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.252608
obj = -0.951786, rho = -0.006367
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.201237
obj = -1.079354, rho = 0.020118
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..........*
optimization finished, #iter = 1047
nu = 0.158360
obj = -1.212011, rho = 0.056450
nSV = 22, nBSV = 10
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 193
nu = 0.127211
obj = -1.370363, rho = -0.044690
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.103852
obj = -1.485168, rho = -0.059708
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.075417
obj = -1.576452, rho = -0.083462
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.057795
obj = -1.663578, rho = -0.213662
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.042495
obj = -1.708999, rho = -0.244719
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.030358
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.021105
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.014672
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.010200
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.007091
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.004930
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.003427
obj = -1.713650, rho = -0.238407
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.600000
obj = -0.404767, rho = -0.101951
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.496247
obj = -0.490087, rho = -0.098106
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.426699
obj = -0.596901, rho = -0.100661
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.365426
obj = -0.718134, rho = -0.074604
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.304941
obj = -0.843053, rho = -0.062960
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.249116
obj = -0.994676, rho = -0.030866
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.203136
obj = -1.182241, rho = -0.007712
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.169650
obj = -1.387125, rho = 0.069359
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.141727
obj = -1.619724, rho = 0.025681
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.116272
obj = -1.843649, rho = 0.063109
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.090282
obj = -2.097499, rho = -0.028691
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 362
nu = 0.071965
obj = -2.399037, rho = 0.067870
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 272
nu = 0.056512
obj = -2.755123, rho = 0.133538
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
....*.*
optimization finished, #iter = 553
nu = 0.051476
obj = -3.040698, rho = 0.629169
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 313
nu = 0.037573
obj = -3.141339, rho = 0.631607
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*..*
optimization finished, #iter = 415
nu = 0.027105
obj = -3.165685, rho = 0.631346
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*..*
optimization finished, #iter = 415
nu = 0.018843
obj = -3.165685, rho = 0.631346
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*..*
optimization finished, #iter = 415
nu = 0.013100
obj = -3.165685, rho = 0.631346
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*..*
optimization finished, #iter = 415
nu = 0.009107
obj = -3.165685, rho = 0.631346
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*..*
optimization finished, #iter = 415
nu = 0.006331
obj = -3.165685, rho = 0.631346
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.647127
obj = -0.451275, rho = 0.017834
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.560000
obj = -0.551762, rho = 0.046589
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.481311
obj = -0.666799, rho = 0.030038
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.404498
obj = -0.804219, rho = -0.004863
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.336362
obj = -0.962752, rho = -0.016277
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.280845
obj = -1.159214, rho = -0.054144
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.237383
obj = -1.391471, rho = -0.030067
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.203392
obj = -1.644545, rho = 0.123834
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.166931
obj = -1.906350, rho = 0.138053
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.132236
obj = -2.201631, rho = 0.095146
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*..*
optimization finished, #iter = 431
nu = 0.107766
obj = -2.569039, rho = 0.098998
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 397
nu = 0.084624
obj = -3.017469, rho = 0.080296
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.072525
obj = -3.567929, rho = 0.024065
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.065564
obj = -3.965456, rho = -0.310440
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
...*
optimization finished, #iter = 392
nu = 0.049305
obj = -4.002797, rho = -0.391876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 392
nu = 0.034276
obj = -4.002797, rho = -0.391876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 392
nu = 0.023829
obj = -4.002797, rho = -0.391876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 392
nu = 0.016566
obj = -4.002797, rho = -0.391876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 392
nu = 0.011516
obj = -4.002797, rho = -0.391876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 392
nu = 0.008006
obj = -4.002797, rho = -0.391876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.591439
obj = -0.402086, rho = -0.109426
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.507739
obj = -0.484127, rho = -0.210116
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.431215
obj = -0.575202, rho = -0.157248
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.347884
obj = -0.678071, rho = -0.171279
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.292657
obj = -0.803775, rho = -0.220241
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.238565
obj = -0.948697, rho = -0.197114
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.193976
obj = -1.114484, rho = -0.229300
nSV = 24, nBSV = 13
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.157342
obj = -1.328207, rho = -0.215321
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.129480
obj = -1.586592, rho = -0.207718
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.106948
obj = -1.914502, rho = -0.174460
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*.*
optimization finished, #iter = 157
nu = 0.091469
obj = -2.297972, rho = -0.208236
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.079452
obj = -2.712645, rho = -0.303820
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 295
nu = 0.062484
obj = -3.157654, rho = -0.325755
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.051746
obj = -3.710072, rho = -0.361909
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.043715
obj = -4.269229, rho = -0.278487
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.037593
obj = -4.633551, rho = -0.212424
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.027681
obj = -4.650483, rho = -0.215587
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.019244
obj = -4.650483, rho = -0.215587
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.013378
obj = -4.650483, rho = -0.215587
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.009300
obj = -4.650483, rho = -0.215587
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.598646
obj = -0.395688, rho = -0.028464
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.496987
obj = -0.472487, rho = -0.008431
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.423864
obj = -0.557487, rho = -0.128685
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.344642
obj = -0.647551, rho = -0.175684
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.286049
obj = -0.744280, rho = -0.128561
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.224145
obj = -0.855508, rho = -0.123222
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.182278
obj = -0.980236, rho = -0.094066
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.142177
obj = -1.117388, rho = -0.144816
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.113799
obj = -1.284239, rho = -0.254412
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.092620
obj = -1.454114, rho = -0.255127
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.072560
obj = -1.632873, rho = -0.265568
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 271
nu = 0.055133
obj = -1.852492, rho = -0.253132
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.043808
obj = -2.138899, rho = -0.211635
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.036890
obj = -2.440446, rho = -0.109910
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 287
nu = 0.031702
obj = -2.587801, rho = 0.007957
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 457
nu = 0.022158
obj = -2.587963, rho = 0.005844
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 457
nu = 0.015404
obj = -2.587963, rho = 0.005844
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 457
nu = 0.010709
obj = -2.587963, rho = 0.005844
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 457
nu = 0.007445
obj = -2.587963, rho = 0.005844
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 457
nu = 0.005176
obj = -2.587963, rho = 0.005844
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.605657
obj = -0.405504, rho = -0.043909
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.511924
obj = -0.486492, rho = 0.026683
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.421644
obj = -0.583292, rho = -0.018409
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.348920
obj = -0.699720, rho = -0.080809
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.291849
obj = -0.846827, rho = -0.104192
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.246618
obj = -1.023142, rho = -0.081692
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.207648
obj = -1.242445, rho = 0.020360
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.172767
obj = -1.504423, rho = 0.048231
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.149754
obj = -1.818818, rho = -0.112365
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.124025
obj = -2.179743, rho = -0.168594
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.106862
obj = -2.566119, rho = -0.428999
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.086764
obj = -2.985097, rho = -0.442254
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.068747
obj = -3.501530, rho = -0.314500
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*
optimization finished, #iter = 343
nu = 0.055315
obj = -4.149595, rho = -0.321869
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.045155
obj = -5.010108, rho = -0.437959
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.039670
obj = -6.032775, rho = -0.962237
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.035521
obj = -6.957657, rho = -1.540230
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*..*
optimization finished, #iter = 313
nu = 0.030443
obj = -7.356358, rho = -2.079637
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*..*
optimization finished, #iter = 313
nu = 0.021164
obj = -7.356358, rho = -2.079637
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*..*
optimization finished, #iter = 313
nu = 0.014713
obj = -7.356358, rho = -2.079637
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.634823
obj = -0.430783, rho = 0.027043
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.544779
obj = -0.514931, rho = 0.091152
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.454027
obj = -0.611540, rho = 0.055925
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.377925
obj = -0.721460, rho = 0.087325
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.307138
obj = -0.846014, rho = 0.115953
nSV = 37, nBSV = 27
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 110
nu = 0.251527
obj = -0.989763, rho = 0.149280
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.205784
obj = -1.158368, rho = 0.139229
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.163047
obj = -1.358931, rho = 0.156211
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.131331
obj = -1.630692, rho = 0.172711
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.116792
obj = -1.937016, rho = 0.391495
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.100000
obj = -2.220351, rho = 0.563598
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..**.*
optimization finished, #iter = 350
nu = 0.080983
obj = -2.381944, rho = 0.822437
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 257
nu = 0.062463
obj = -2.476574, rho = 1.048476
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.043988
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.030580
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.021259
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.014779
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.010274
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.007143
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 448
nu = 0.004965
obj = -2.482966, rho = 1.074039
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.550930
obj = -0.363987, rho = -0.204786
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.460000
obj = -0.434490, rho = -0.170802
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.378277
obj = -0.515312, rho = -0.175537
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.318940
obj = -0.610138, rho = -0.156556
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 248
nu = 0.263641
obj = -0.712949, rho = -0.057575
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.210407
obj = -0.836497, rho = -0.065734
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.171553
obj = -0.992127, rho = -0.068383
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.141401
obj = -1.178664, rho = -0.076546
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.116725
obj = -1.404627, rho = -0.076582
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.095207
obj = -1.674820, rho = -0.186625
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.078139
obj = -2.036857, rho = -0.249054
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*...*
optimization finished, #iter = 429
nu = 0.068382
obj = -2.438642, rho = -0.411642
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.059403
obj = -2.867786, rho = -0.655274
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.049186
obj = -3.196276, rho = -0.809197
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.041667
obj = -3.398070, rho = -0.718138
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.029098
obj = -3.398177, rho = -0.715695
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.020229
obj = -3.398177, rho = -0.715695
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.014063
obj = -3.398177, rho = -0.715695
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.009777
obj = -3.398177, rho = -0.715695
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.006797
obj = -3.398177, rho = -0.715695
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.583163
obj = -0.391556, rho = -0.055452
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.490054
obj = -0.471931, rho = -0.113829
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.404971
obj = -0.569897, rho = -0.135519
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.344824
obj = -0.689506, rho = -0.098905
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.290098
obj = -0.825121, rho = -0.144485
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.242121
obj = -0.988723, rho = -0.174158
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.198535
obj = -1.192796, rho = -0.247341
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.163327
obj = -1.452674, rho = -0.309485
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.143106
obj = -1.765984, rho = -0.252840
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.121609
obj = -2.118753, rho = -0.218798
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.102122
obj = -2.517950, rho = -0.351305
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.084823
obj = -2.959920, rho = -0.494409
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.067687
obj = -3.481902, rho = -0.411270
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.053706
obj = -4.195209, rho = -0.346828
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.044757
obj = -5.180346, rho = -0.138069
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.039110
obj = -6.418791, rho = 0.018146
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.034981
obj = -7.833177, rho = 0.142444
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.032658
obj = -8.952595, rho = 0.127111
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.026516
obj = -9.219510, rho = 0.060163
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.018434
obj = -9.219510, rho = 0.060163
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.538978
obj = -0.359331, rho = -0.138370
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.445010
obj = -0.432816, rho = -0.140752
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.374337
obj = -0.521443, rho = -0.166139
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.315575
obj = -0.630472, rho = -0.168183
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.264497
obj = -0.755749, rho = -0.166769
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.227784
obj = -0.888000, rho = -0.105573
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.187723
obj = -1.027319, rho = -0.098832
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 243
nu = 0.145956
obj = -1.196009, rho = -0.126961
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.123306
obj = -1.392104, rho = -0.291964
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.095481
obj = -1.609587, rho = -0.325455
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.083921
obj = -1.846616, rho = -0.473823
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*....*
optimization finished, #iter = 442
nu = 0.066588
obj = -2.037280, rho = -0.551536
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.051770
obj = -2.178970, rho = -0.691328
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.039407
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.027396
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.019045
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.013240
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.009204
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.006399
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*....*
optimization finished, #iter = 1006
nu = 0.004448
obj = -2.224551, rho = -0.777011
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.566292
obj = -0.381695, rho = -0.114758
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.477333
obj = -0.457553, rho = -0.095659
nSV = 53, nBSV = 44
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.400237
obj = -0.547815, rho = -0.089241
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.329327
obj = -0.657795, rho = -0.093145
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.276184
obj = -0.789914, rho = -0.110513
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.229593
obj = -0.952948, rho = -0.110121
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.197960
obj = -1.133332, rho = -0.306534
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.157286
obj = -1.346560, rho = -0.310752
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.130052
obj = -1.632416, rho = -0.294884
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.110763
obj = -1.966718, rho = -0.320171
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.090867
obj = -2.394214, rho = -0.374193
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.079765
obj = -2.898355, rho = -0.791991
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.067241
obj = -3.474347, rho = -0.936327
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.056512
obj = -4.101961, rho = -1.137650
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.047791
obj = -4.803538, rho = -1.608697
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*..*
optimization finished, #iter = 301
nu = 0.041752
obj = -5.354877, rho = -2.114220
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
..*.*
optimization finished, #iter = 359
nu = 0.032290
obj = -5.424942, rho = -2.370075
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
..*.*
optimization finished, #iter = 359
nu = 0.022448
obj = -5.424942, rho = -2.370075
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
..*.*
optimization finished, #iter = 359
nu = 0.015606
obj = -5.424942, rho = -2.370075
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
..*.*
optimization finished, #iter = 359
nu = 0.010849
obj = -5.424942, rho = -2.370075
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.651598
obj = -0.436497, rho = -0.241312
nSV = 68, nBSV = 61
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.543073
obj = -0.526384, rho = -0.294832
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.453558
obj = -0.636176, rho = -0.250875
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.380615
obj = -0.768024, rho = -0.253719
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.323487
obj = -0.927162, rho = -0.361499
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.273562
obj = -1.102805, rho = -0.457766
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.232183
obj = -1.292637, rho = -0.385937
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.191399
obj = -1.490389, rho = -0.387634
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.156201
obj = -1.687929, rho = -0.423321
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.....*
optimization finished, #iter = 554
nu = 0.120054
obj = -1.890353, rho = -0.428203
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*......*
optimization finished, #iter = 838
nu = 0.091651
obj = -2.146728, rho = -0.420292
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.075077
obj = -2.451244, rho = -0.534180
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.060253
obj = -2.727364, rho = -0.700567
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.048783
obj = -2.896868, rho = -0.724898
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.036419
obj = -2.956998, rho = -0.732247
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.025318
obj = -2.956998, rho = -0.732247
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.017601
obj = -2.956998, rho = -0.732247
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.012236
obj = -2.956998, rho = -0.732247
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.008507
obj = -2.956998, rho = -0.732247
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.005914
obj = -2.956998, rho = -0.732247
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.553423
obj = -0.363050, rho = -0.203758
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.465331
obj = -0.429438, rho = -0.216850
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.377742
obj = -0.507373, rho = -0.206336
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.315233
obj = -0.597913, rho = -0.143202
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.259787
obj = -0.696470, rho = -0.192545
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.209254
obj = -0.804756, rho = -0.188489
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.166386
obj = -0.929046, rho = -0.168794
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.140756
obj = -1.054359, rho = -0.224722
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.114029
obj = -1.162033, rho = -0.238712
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.085345
obj = -1.240439, rho = -0.285940
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.063730
obj = -1.336189, rho = -0.301737
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.050134
obj = -1.367660, rho = -0.314860
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.034851
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.024228
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.016843
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.011709
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.008140
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.005659
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.003934
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.002735
obj = -1.367660, rho = -0.314775
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.560865
obj = -0.386331, rho = -0.206652
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.479153
obj = -0.468394, rho = -0.154207
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.396844
obj = -0.568144, rho = -0.156626
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.340108
obj = -0.694423, rho = -0.112515
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.290899
obj = -0.840973, rho = -0.106255
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.245091
obj = -1.017733, rho = -0.089993
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.209992
obj = -1.222379, rho = -0.176823
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.175386
obj = -1.442258, rho = -0.220559
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.142909
obj = -1.699656, rho = -0.160072
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.118865
obj = -1.992000, rho = -0.068086
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.098754
obj = -2.338581, rho = -0.085290
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.087357
obj = -2.574132, rho = -0.253505
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*...*
optimization finished, #iter = 312
nu = 0.065010
obj = -2.669073, rho = -0.145933
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*
optimization finished, #iter = 367
nu = 0.046668
obj = -2.768070, rho = -0.089159
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.034747
obj = -2.820955, rho = 0.056471
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.024156
obj = -2.820955, rho = 0.056471
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.016793
obj = -2.820955, rho = 0.056471
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.011674
obj = -2.820955, rho = 0.056471
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.008116
obj = -2.820955, rho = 0.056471
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.005642
obj = -2.820955, rho = 0.056471
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.625758
obj = -0.407758, rho = 0.117114
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.518486
obj = -0.481039, rho = 0.109154
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.421144
obj = -0.568339, rho = 0.074951
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.348209
obj = -0.674163, rho = 0.150335
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.278165
obj = -0.802155, rho = 0.141631
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.233253
obj = -0.966951, rho = 0.084959
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.196806
obj = -1.171522, rho = 0.083727
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.163297
obj = -1.408586, rho = 0.029434
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.138157
obj = -1.707962, rho = 0.114108
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.117366
obj = -2.040616, rho = 0.280485
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.095038
obj = -2.456440, rho = 0.352739
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.081362
obj = -2.972253, rho = 0.433734
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.065531
obj = -3.617268, rho = 0.479816
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.053776
obj = -4.531933, rho = 0.423996
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.......*
optimization finished, #iter = 831
nu = 0.045502
obj = -5.841552, rho = 0.397234
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 356
nu = 0.040298
obj = -7.716889, rho = 0.450761
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*...........*
optimization finished, #iter = 1265
nu = 0.037660
obj = -10.207765, rho = 0.559203
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
..*.*
optimization finished, #iter = 314
nu = 0.035814
obj = -13.350564, rho = 0.727799
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.032605
obj = -17.011480, rho = 0.893405
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.028579
obj = -21.706584, rho = 1.235971
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.607313
obj = -0.404037, rho = -0.113556
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.508460
obj = -0.485514, rho = -0.101954
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.430160
obj = -0.578769, rho = -0.155607
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.348573
obj = -0.688000, rho = -0.208542
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.301763
obj = -0.809061, rho = -0.289907
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.239559
obj = -0.943954, rho = -0.280931
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.199516
obj = -1.097313, rho = -0.485149
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.158548
obj = -1.270235, rho = -0.567265
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.135178
obj = -1.446029, rho = -0.651081
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*..*
optimization finished, #iter = 293
nu = 0.105875
obj = -1.584050, rho = -0.630296
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 362
nu = 0.079357
obj = -1.736718, rho = -0.607450
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.060260
obj = -1.928386, rho = -0.643089
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.048048
obj = -2.124904, rho = -0.876144
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.036772
obj = -2.287097, rho = -1.114211
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.029134
obj = -2.365679, rho = -1.405346
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.020253
obj = -2.365679, rho = -1.405346
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.014080
obj = -2.365679, rho = -1.405346
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.009788
obj = -2.365679, rho = -1.405346
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.006805
obj = -2.365679, rho = -1.405346
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.004731
obj = -2.365679, rho = -1.405346
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.554265
obj = -0.382083, rho = 0.162590
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.472059
obj = -0.464388, rho = 0.271607
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.398655
obj = -0.562559, rho = 0.233925
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.343705
obj = -0.677516, rho = 0.176573
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.285289
obj = -0.811770, rho = 0.223812
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.240437
obj = -0.965541, rho = 0.300297
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.195943
obj = -1.141615, rho = 0.392505
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.162929
obj = -1.347652, rho = 0.422306
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*..*
optimization finished, #iter = 212
nu = 0.134766
obj = -1.596692, rho = 0.531028
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.110045
obj = -1.884739, rho = 0.556214
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.095331
obj = -2.185429, rho = 0.532104
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.080925
obj = -2.394147, rho = 0.159733
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*........*
optimization finished, #iter = 1184
nu = 0.061441
obj = -2.488717, rho = -0.044907
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.044338
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.030824
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.021428
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.014897
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.010356
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.007200
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 650
nu = 0.005005
obj = -2.502750, rho = -0.143768
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.568094
obj = -0.394229, rho = -0.132107
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.498624
obj = -0.481625, rho = -0.093607
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.411124
obj = -0.581993, rho = -0.085050
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.342415
obj = -0.712863, rho = -0.126276
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.292631
obj = -0.881256, rho = -0.174611
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.258714
obj = -1.075438, rho = -0.269076
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.217718
obj = -1.288230, rho = -0.191939
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.183955
obj = -1.534773, rho = -0.288380
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.150312
obj = -1.835212, rho = -0.269641
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.123120
obj = -2.217667, rho = -0.332355
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.103634
obj = -2.680693, rho = -0.607604
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.093086
obj = -3.184253, rho = -0.947109
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.080535
obj = -3.546609, rho = -1.199136
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.061090
obj = -3.841770, rho = -1.256413
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.047924
obj = -4.005829, rho = -1.406960
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*..*
optimization finished, #iter = 403
nu = 0.034648
obj = -4.046717, rho = -1.386390
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 403
nu = 0.024087
obj = -4.046717, rho = -1.386390
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 403
nu = 0.016745
obj = -4.046717, rho = -1.386390
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 403
nu = 0.011641
obj = -4.046717, rho = -1.386390
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 403
nu = 0.008093
obj = -4.046717, rho = -1.386390
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.609218
obj = -0.422584, rho = -0.067662
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.523085
obj = -0.517273, rho = -0.109968
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.454800
obj = -0.629620, rho = -0.132517
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.381942
obj = -0.756849, rho = -0.173350
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.317460
obj = -0.906681, rho = -0.154977
nSV = 36, nBSV = 25
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.259580
obj = -1.094214, rho = -0.120674
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.217181
obj = -1.336083, rho = -0.090451
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.188038
obj = -1.615162, rho = -0.090483
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.161236
obj = -1.937353, rho = -0.139912
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 350
nu = 0.130319
obj = -2.322601, rho = -0.153193
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.109493
obj = -2.794183, rho = -0.157890
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.090884
obj = -3.375624, rho = -0.098452
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 365
nu = 0.074639
obj = -4.139330, rho = -0.088455
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 285
nu = 0.068375
obj = -5.026840, rho = -0.250838
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.059194
obj = -5.888653, rho = -0.207941
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*....*.*
optimization finished, #iter = 734
nu = 0.051367
obj = -6.441632, rho = -0.153021
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
............*.......*
optimization finished, #iter = 1917
nu = 0.039525
obj = -6.640475, rho = -0.018814
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
............*.......*
optimization finished, #iter = 1917
nu = 0.027478
obj = -6.640475, rho = -0.018814
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
............*.......*
optimization finished, #iter = 1917
nu = 0.019102
obj = -6.640475, rho = -0.018814
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
............*.......*
optimization finished, #iter = 1917
nu = 0.013280
obj = -6.640475, rho = -0.018814
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.600151
obj = -0.396616, rho = -0.199145
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.504668
obj = -0.469730, rho = -0.257968
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.408374
obj = -0.558283, rho = -0.229798
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.340690
obj = -0.664142, rho = -0.192925
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.279814
obj = -0.794401, rho = -0.215144
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.233483
obj = -0.944426, rho = -0.221741
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.189770
obj = -1.124844, rho = -0.181805
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.156553
obj = -1.359105, rho = -0.183505
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.137818
obj = -1.631437, rho = -0.339011
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.116249
obj = -1.913488, rho = -0.562404
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.098975
obj = -2.163948, rho = -0.724658
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 331
nu = 0.075314
obj = -2.401007, rho = -0.758531
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.057755
obj = -2.672360, rho = -0.706243
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 233
nu = 0.045369
obj = -2.974999, rho = -0.628965
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*........*
optimization finished, #iter = 987
nu = 0.036525
obj = -3.231402, rho = -0.519611
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.028260
obj = -3.300466, rho = -0.486374
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.019646
obj = -3.300466, rho = -0.486374
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.013658
obj = -3.300466, rho = -0.486374
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.009495
obj = -3.300466, rho = -0.486374
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.006601
obj = -3.300466, rho = -0.486374
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.576129
obj = -0.412096, rho = -0.224817
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.501939
obj = -0.512673, rho = -0.161823
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.444376
obj = -0.628008, rho = -0.073951
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.370666
obj = -0.766088, rho = -0.036468
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.319734
obj = -0.928431, rho = -0.056200
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.273633
obj = -1.123105, rho = -0.144010
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.236063
obj = -1.347047, rho = -0.131188
nSV = 25, nBSV = 21
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.195396
obj = -1.568522, rho = -0.143766
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 184
nu = 0.157305
obj = -1.826295, rho = -0.226709
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.....*
optimization finished, #iter = 593
nu = 0.129744
obj = -2.112124, rho = -0.367044
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.....*
optimization finished, #iter = 630
nu = 0.102534
obj = -2.417526, rho = -0.426577
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.081473
obj = -2.803608, rho = -0.408181
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.067292
obj = -3.246757, rho = -0.296808
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*
optimization finished, #iter = 297
nu = 0.057435
obj = -3.628383, rho = -0.099091
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 354
nu = 0.044080
obj = -3.893110, rho = -0.057570
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 324
nu = 0.033880
obj = -4.070233, rho = -0.398319
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.024275
obj = -4.078967, rho = -0.401839
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.016876
obj = -4.078967, rho = -0.401839
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.011732
obj = -4.078967, rho = -0.401839
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.008156
obj = -4.078967, rho = -0.401839
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.600866
obj = -0.412104, rho = -0.034511
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.511690
obj = -0.501099, rho = 0.023548
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.434635
obj = -0.608304, rho = 0.117299
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.370758
obj = -0.727716, rho = 0.196824
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.306553
obj = -0.869946, rho = 0.156269
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.253825
obj = -1.040293, rho = 0.110573
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.214792
obj = -1.243189, rho = 0.145258
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.183857
obj = -1.449963, rho = 0.346841
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.143611
obj = -1.690152, rho = 0.380385
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.116570
obj = -1.975060, rho = 0.355531
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.096969
obj = -2.326008, rho = 0.352494
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.087299
obj = -2.580927, rho = -0.008457
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*..*
optimization finished, #iter = 556
nu = 0.067361
obj = -2.647999, rho = -0.144073
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*.*
optimization finished, #iter = 573
nu = 0.046925
obj = -2.651976, rho = -0.156664
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 678
nu = 0.032663
obj = -2.652056, rho = -0.170199
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 678
nu = 0.022707
obj = -2.652056, rho = -0.170199
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 678
nu = 0.015786
obj = -2.652056, rho = -0.170199
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 678
nu = 0.010974
obj = -2.652056, rho = -0.170199
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 678
nu = 0.007629
obj = -2.652056, rho = -0.170199
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 678
nu = 0.005304
obj = -2.652056, rho = -0.170199
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.526715
obj = -0.346732, rho = -0.218891
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.438716
obj = -0.412487, rho = -0.210928
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.369417
obj = -0.484235, rho = -0.182211
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.301424
obj = -0.563114, rho = -0.187899
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.246247
obj = -0.647418, rho = -0.172488
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.194688
obj = -0.742829, rho = -0.122516
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.155719
obj = -0.855108, rho = -0.172944
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.128137
obj = -0.971276, rho = -0.164155
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.099663
obj = -1.094247, rho = -0.156934
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 275
nu = 0.076375
obj = -1.237870, rho = -0.165275
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.058678
obj = -1.440287, rho = -0.159197
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.050049
obj = -1.684136, rho = -0.023565
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.042416
obj = -1.918468, rho = -0.295460
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.035437
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.024635
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.017126
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.011906
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.008277
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.005754
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.004000
obj = -2.000346, rho = -0.608835
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.562035
obj = -0.379576, rho = -0.243606
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.472239
obj = -0.457651, rho = -0.305310
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.395984
obj = -0.549880, rho = -0.309892
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.332139
obj = -0.662067, rho = -0.319246
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.281963
obj = -0.793231, rho = -0.310976
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.234359
obj = -0.944775, rho = -0.307920
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.191964
obj = -1.122992, rho = -0.355744
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.158206
obj = -1.332611, rho = -0.368394
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 323
nu = 0.128376
obj = -1.608230, rho = -0.364307
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.108247
obj = -1.951630, rho = -0.224496
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.090001
obj = -2.385905, rho = -0.210757
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.075772
obj = -2.943621, rho = -0.186912
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.064895
obj = -3.672880, rho = -0.157989
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.055899
obj = -4.605941, rho = -0.291741
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.049713
obj = -5.778115, rho = -0.465499
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.044631
obj = -7.077030, rho = -0.629946
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.039423
obj = -8.510594, rho = -0.295701
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.035996
obj = -9.706996, rho = -0.397316
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.028373
obj = -9.862439, rho = -0.486877
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.019724
obj = -9.862439, rho = -0.486877
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.573414
obj = -0.381352, rho = -0.048900
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.477505
obj = -0.455802, rho = 0.031279
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.397085
obj = -0.545496, rho = 0.058365
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.337682
obj = -0.648256, rho = 0.044917
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.279437
obj = -0.761108, rho = 0.075513
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.227268
obj = -0.888173, rho = 0.101217
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.191431
obj = -1.011321, rho = -0.011244
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.152725
obj = -1.129457, rho = 0.003252
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.121957
obj = -1.227903, rho = -0.028839
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.091549
obj = -1.290421, rho = 0.012678
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 311
nu = 0.067937
obj = -1.332339, rho = 0.006837
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*...*
optimization finished, #iter = 402
nu = 0.048982
obj = -1.348869, rho = -0.015062
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.034525
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.024001
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.016686
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.011600
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.008064
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.005606
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.003897
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.002709
obj = -1.354686, rho = -0.002037
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.594470
obj = -0.407468, rho = -0.101781
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.501928
obj = -0.498318, rho = -0.115355
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.428771
obj = -0.609848, rho = -0.098731
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.379743
obj = -0.737838, rho = -0.163570
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.312679
obj = -0.872544, rho = -0.126469
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.257153
obj = -1.039949, rho = -0.201463
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.210868
obj = -1.238556, rho = -0.173589
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.172078
obj = -1.484966, rho = -0.194924
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 267
nu = 0.143989
obj = -1.798701, rho = -0.169567
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 269
nu = 0.119305
obj = -2.191754, rho = -0.123403
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.100428
obj = -2.710727, rho = -0.150724
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.089683
obj = -3.342097, rho = -0.195304
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.080636
obj = -3.961358, rho = -0.297570
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.069486
obj = -4.465427, rho = -0.445774
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*..*
optimization finished, #iter = 230
nu = 0.053640
obj = -4.790346, rho = -0.654343
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.041415
obj = -5.101814, rho = -0.812384
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.030531
obj = -5.129127, rho = -0.887838
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.021225
obj = -5.129127, rho = -0.887838
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.014755
obj = -5.129127, rho = -0.887838
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.010258
obj = -5.129127, rho = -0.887838
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.512257
obj = -0.348175, rho = -0.138531
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.439188
obj = -0.417046, rho = -0.083291
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.373268
obj = -0.486238, rho = 0.036055
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.303266
obj = -0.563066, rho = 0.014957
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.247136
obj = -0.640016, rho = 0.083462
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*...*
optimization finished, #iter = 324
nu = 0.191644
obj = -0.726415, rho = 0.115633
nSV = 25, nBSV = 14
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.153511
obj = -0.833536, rho = 0.105195
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.125221
obj = -0.956650, rho = 0.221241
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.099672
obj = -1.065066, rho = 0.304493
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.075020
obj = -1.197118, rho = 0.346486
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.062819
obj = -1.326895, rho = 0.460333
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*...*
optimization finished, #iter = 375
nu = 0.048299
obj = -1.403754, rho = 0.458105
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.036248
obj = -1.441276, rho = 0.525945
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.025561
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.017770
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.012354
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.008588
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.005970
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.004151
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.002885
obj = -1.442637, rho = 0.554009
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.640000
obj = -0.451697, rho = -0.146373
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.554801
obj = -0.554164, rho = -0.170066
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.465778
obj = -0.679296, rho = -0.185852
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.400569
obj = -0.838778, rho = -0.137235
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.349310
obj = -1.030916, rho = -0.119464
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.297445
obj = -1.254246, rho = -0.124432
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.249661
obj = -1.525268, rho = -0.055561
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.214806
obj = -1.855467, rho = -0.101156
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.176632
obj = -2.255641, rho = -0.059871
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.151522
obj = -2.769397, rho = -0.111309
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 268
nu = 0.126724
obj = -3.403969, rho = -0.109062
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.108335
obj = -4.229664, rho = 0.019216
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 398
nu = 0.091149
obj = -5.290166, rho = 0.051325
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.080172
obj = -6.757910, rho = 0.137453
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.076385
obj = -8.416262, rho = 0.397338
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.066920
obj = -9.928755, rho = 0.532729
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
........*........*
optimization finished, #iter = 1670
nu = 0.052430
obj = -11.754918, rho = 0.487612
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.043696
obj = -14.288479, rho = 0.631199
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.
WARNING: using -h 0 may be faster
*.*
optimization finished, #iter = 209
nu = 0.041253
obj = -16.591671, rho = 1.201108
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
..*.*
optimization finished, #iter = 384
nu = 0.034200
obj = -17.104087, rho = 1.580887
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.614650
obj = -0.419324, rho = -0.176891
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.523205
obj = -0.505094, rho = -0.134619
nSV = 57, nBSV = 49
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.438549
obj = -0.609287, rho = -0.082178
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.365878
obj = -0.738332, rho = -0.072779
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.306905
obj = -0.891787, rho = -0.062874
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.261099
obj = -1.082546, rho = -0.007297
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.221630
obj = -1.299580, rho = -0.075618
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.178115
obj = -1.563707, rho = -0.081920
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.149352
obj = -1.925325, rho = 0.073544
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.127451
obj = -2.375827, rho = 0.167538
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.109594
obj = -2.947830, rho = 0.167663
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.093817
obj = -3.669710, rho = 0.142853
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 272
nu = 0.081025
obj = -4.576595, rho = 0.056499
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 306
nu = 0.067723
obj = -5.783285, rho = 0.060503
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 235
nu = 0.058494
obj = -7.477316, rho = 0.081172
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.052384
obj = -9.792521, rho = 0.151811
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.048217
obj = -12.855917, rho = 0.209792
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.046283
obj = -16.593892, rho = 0.081498
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 369
nu = 0.045329
obj = -20.373710, rho = -0.299811
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 603
nu = 0.037612
obj = -23.635348, rho = -0.574441
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.586334
obj = -0.391005, rho = -0.260339
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.486135
obj = -0.470056, rho = -0.198431
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.409686
obj = -0.560825, rho = -0.167969
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.336206
obj = -0.670983, rho = -0.154899
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.291755
obj = -0.796166, rho = -0.039241
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.234947
obj = -0.929503, rho = 0.025219
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.190369
obj = -1.099562, rho = 0.053127
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 214
nu = 0.151683
obj = -1.315991, rho = 0.088229
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.129323
obj = -1.595350, rho = 0.192298
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.110717
obj = -1.904640, rho = 0.284374
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.089241
obj = -2.277811, rho = 0.257582
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.074364
obj = -2.734746, rho = 0.133152
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.061027
obj = -3.330002, rho = 0.047707
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.051148
obj = -4.123323, rho = 0.032208
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.045712
obj = -5.104887, rho = -0.174498
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*..*
optimization finished, #iter = 300
nu = 0.038102
obj = -6.199914, rho = -0.240263
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.035439
obj = -7.427579, rho = -0.214559
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.033444
obj = -8.083831, rho = -0.272281
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.023250
obj = -8.083831, rho = -0.272281
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.016163
obj = -8.083831, rho = -0.272281
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.574097
obj = -0.384960, rho = -0.068041
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.484172
obj = -0.462823, rho = -0.034170
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.412757
obj = -0.550637, rho = -0.011283
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.340170
obj = -0.644759, rho = -0.056938
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.278368
obj = -0.756184, rho = -0.130685
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.227864
obj = -0.886299, rho = -0.170354
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.180179
obj = -1.041596, rho = -0.140075
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.148161
obj = -1.227237, rho = -0.152249
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.122527
obj = -1.454755, rho = -0.175410
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.107710
obj = -1.682442, rho = 0.048066
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.081879
obj = -1.895140, rho = 0.065719
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.064137
obj = -2.169698, rho = 0.130408
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.051072
obj = -2.518153, rho = 0.273546
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.043065
obj = -2.869739, rho = 0.512707
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.037579
obj = -3.051108, rho = 0.855929
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.026124
obj = -3.051108, rho = 0.855929
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.018161
obj = -3.051108, rho = 0.855929
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.012626
obj = -3.051108, rho = 0.855929
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.008777
obj = -3.051108, rho = 0.855929
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.006102
obj = -3.051108, rho = 0.855929
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.626751
obj = -0.407036, rho = 0.011936
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.506800
obj = -0.483551, rho = 0.018969
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.427336
obj = -0.576843, rho = -0.039225
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.356468
obj = -0.681282, rho = 0.023879
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.288770
obj = -0.801745, rho = 0.095994
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.245815
obj = -0.930719, rho = 0.160510
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.196350
obj = -1.065957, rho = 0.207821
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.159940
obj = -1.200569, rho = 0.292410
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.123561
obj = -1.347635, rho = 0.335359
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.099113
obj = -1.486017, rho = 0.290400
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.075972
obj = -1.610632, rho = 0.227215
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.058946
obj = -1.697132, rho = 0.216146
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.044034
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.030612
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.021281
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.014795
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.010285
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.007150
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.004971
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.003456
obj = -1.728045, rho = 0.192771
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.640000
obj = -0.436577, rho = 0.059010
nSV = 65, nBSV = 63
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.545143
obj = -0.527055, rho = 0.036236
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.454933
obj = -0.636201, rho = 0.017609
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.375036
obj = -0.772966, rho = 0.047787
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.322200
obj = -0.940804, rho = -0.065314
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.270647
obj = -1.141788, rho = -0.002409
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.230949
obj = -1.380016, rho = -0.028955
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.190900
obj = -1.682498, rho = -0.002579
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.162727
obj = -2.055528, rho = -0.110137
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.136507
obj = -2.511129, rho = 0.002211
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.114797
obj = -3.111324, rho = 0.020186
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.100248
obj = -3.863371, rho = 0.085802
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.085395
obj = -4.758860, rho = 0.150461
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.076480
obj = -5.862502, rho = 0.109518
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.068392
obj = -6.888480, rho = 0.046379
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.060156
obj = -7.607395, rho = 0.846590
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.046160
obj = -7.755996, rho = 1.146709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.032090
obj = -7.755996, rho = 1.146709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.022309
obj = -7.755996, rho = 1.146709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.015509
obj = -7.755996, rho = 1.146709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.560000
obj = -0.384069, rho = -0.349641
nSV = 56, nBSV = 56
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.480000
obj = -0.467309, rho = -0.361376
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.400000
obj = -0.564375, rho = -0.354095
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.347418
obj = -0.673780, rho = -0.382414
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.285290
obj = -0.795576, rho = -0.392020
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.234430
obj = -0.944757, rho = -0.386202
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.191946
obj = -1.124208, rho = -0.473260
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.155435
obj = -1.350040, rho = -0.484100
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.131866
obj = -1.638774, rho = -0.561572
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.110532
obj = -1.992225, rho = -0.754134
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.094686
obj = -2.393771, rho = -1.075451
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.081265
obj = -2.857701, rho = -1.073814
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.068847
obj = -3.315726, rho = -1.186913
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.056705
obj = -3.736840, rho = -1.346537
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.048866
obj = -3.968093, rho = -1.557918
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.033971
obj = -3.968093, rho = -1.557918
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.023617
obj = -3.968093, rho = -1.557918
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.016418
obj = -3.968093, rho = -1.557918
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.011414
obj = -3.968093, rho = -1.557918
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.007935
obj = -3.968093, rho = -1.557918
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.610011
obj = -0.434600, rho = -0.110974
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.532990
obj = -0.536023, rho = -0.126137
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.454400
obj = -0.660840, rho = -0.053532
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.389559
obj = -0.817207, rho = -0.091479
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.332395
obj = -1.012652, rho = -0.102863
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.282989
obj = -1.257204, rho = -0.034358
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.241905
obj = -1.576843, rho = 0.057461
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.207529
obj = -2.007993, rho = 0.051446
nSV = 28, nBSV = 17
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.182181
obj = -2.584077, rho = -0.016412
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.170987
obj = -3.327000, rho = -0.239397
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.154163
obj = -4.144927, rho = -0.398409
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.**.*
optimization finished, #iter = 185
nu = 0.136890
obj = -5.094112, rho = -0.581461
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.119034
obj = -6.173084, rho = -0.753723
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*.*
optimization finished, #iter = 485
nu = 0.096177
obj = -7.387434, rho = -0.639826
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.....*..........*...*
optimization finished, #iter = 1850
nu = 0.084808
obj = -8.841002, rho = -0.619842
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.......*.*
optimization finished, #iter = 881
nu = 0.067543
obj = -10.519162, rho = -0.639994
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*....*
optimization finished, #iter = 536
nu = 0.057647
obj = -12.466636, rho = -0.593651
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 292
nu = 0.054028
obj = -13.997888, rho = -0.924089
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.041749
obj = -14.515934, rho = -1.202462
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.029024
obj = -14.515934, rho = -1.202462
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.625709
obj = -0.432880, rho = -0.181658
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.540000
obj = -0.526767, rho = -0.198803
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.454978
obj = -0.638439, rho = -0.197084
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.382449
obj = -0.771233, rho = -0.243867
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.328474
obj = -0.923908, rho = -0.252344
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.266551
obj = -1.104268, rho = -0.204505
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.216664
obj = -1.344521, rho = -0.235036
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.185504
obj = -1.653365, rho = -0.164293
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.156566
obj = -2.050329, rho = -0.153569
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.137357
obj = -2.552956, rho = -0.201175
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*.*
optimization finished, #iter = 357
nu = 0.121426
obj = -3.095803, rho = -0.277669
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
....*.*
optimization finished, #iter = 577
nu = 0.097932
obj = -3.782274, rho = -0.263692
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 304
nu = 0.081675
obj = -4.744229, rho = -0.264629
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
....*.*
optimization finished, #iter = 507
nu = 0.068739
obj = -6.093860, rho = -0.264572
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
....*
optimization finished, #iter = 486
nu = 0.059744
obj = -8.035236, rho = -0.264532
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.053912
obj = -10.822527, rho = -0.304759
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.051194
obj = -14.651626, rho = -0.448832
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.049305
obj = -19.668401, rho = -0.656498
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.047991
obj = -25.868435, rho = -0.954839
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.............*
optimization finished, #iter = 1394
nu = 0.043498
obj = -33.434042, rho = -1.102516
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.605153
obj = -0.424700, rho = -0.227395
nSV = 64, nBSV = 57
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.527884
obj = -0.522415, rho = -0.157823
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.448385
obj = -0.634630, rho = -0.256097
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.375704
obj = -0.774963, rho = -0.315969
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.317306
obj = -0.954864, rho = -0.362095
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.272279
obj = -1.171983, rho = -0.326872
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.230912
obj = -1.442890, rho = -0.343181
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.197193
obj = -1.777653, rho = -0.331663
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.168922
obj = -2.206251, rho = -0.301600
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.145444
obj = -2.743456, rho = -0.309382
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 265
nu = 0.128968
obj = -3.373624, rho = -0.384031
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.106811
obj = -4.172238, rho = -0.456772
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*......*
optimization finished, #iter = 807
nu = 0.089259
obj = -5.240845, rho = -0.499539
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.078123
obj = -6.740975, rho = -0.402815
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.069253
obj = -8.627470, rho = -0.267534
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.060260
obj = -11.219603, rho = -0.388938
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.057137
obj = -14.581972, rho = -0.930492
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 329
nu = 0.050785
obj = -18.527171, rho = -1.229834
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.....................*
optimization finished, #iter = 2383
nu = 0.043338
obj = -23.984064, rho = -1.188079
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*
optimization finished, #iter = 343
nu = 0.040610
obj = -31.388353, rho = -1.221074
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.590725
obj = -0.394948, rho = -0.206120
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.496724
obj = -0.476168, rho = -0.161741
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.412198
obj = -0.571186, rho = -0.180088
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.338277
obj = -0.691341, rho = -0.133352
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.284438
obj = -0.843169, rho = -0.181063
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.243847
obj = -1.028420, rho = -0.165691
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.208739
obj = -1.244600, rho = -0.105852
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.177399
obj = -1.501475, rho = -0.075790
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 345
nu = 0.147093
obj = -1.791198, rho = -0.015484
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*
optimization finished, #iter = 259
nu = 0.119363
obj = -2.166118, rho = 0.053115
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.103253
obj = -2.629355, rho = 0.096665
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.083481
obj = -3.195364, rho = 0.083600
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.070182
obj = -3.984149, rho = 0.008077
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.062430
obj = -4.975065, rho = -0.262009
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.059706
obj = -5.933410, rho = -0.520489
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.047722
obj = -6.676773, rho = -0.514136
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.036581
obj = -7.593961, rho = -0.553157
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.032132
obj = -8.432353, rho = -0.430065
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 331
nu = 0.024527
obj = -8.528216, rho = -0.372606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
..*.*
optimization finished, #iter = 331
nu = 0.017051
obj = -8.528216, rho = -0.372606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.605659
obj = -0.434447, rho = -0.218463
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.535136
obj = -0.539836, rho = -0.209920
nSV = 54, nBSV = 52
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.463830
obj = -0.662525, rho = -0.208064
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.399635
obj = -0.815260, rho = -0.271764
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.336508
obj = -0.989981, rho = -0.223359
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.283900
obj = -1.212069, rho = -0.225779
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.244274
obj = -1.466021, rho = -0.222873
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.205615
obj = -1.775296, rho = -0.371605
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.175245
obj = -2.132846, rho = -0.563127
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.152606
obj = -2.484961, rho = -0.633482
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.121566
obj = -2.855380, rho = -0.698511
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.095172
obj = -3.303238, rho = -0.719888
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.078353
obj = -3.838029, rho = -0.756455
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 233
nu = 0.063350
obj = -4.461569, rho = -0.716018
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*..*
optimization finished, #iter = 437
nu = 0.050629
obj = -5.126844, rho = -0.778010
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
...*..*
optimization finished, #iter = 516
nu = 0.040299
obj = -6.003296, rho = -0.826068
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 338
nu = 0.032895
obj = -6.994696, rho = -0.795781
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.028982
obj = -8.011583, rho = -0.670567
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.023628
obj = -8.214003, rho = -0.527492
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.016426
obj = -8.214003, rho = -0.527492
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.555775
obj = -0.377960, rho = -0.018096
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.468777
obj = -0.460008, rho = 0.002024
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.399681
obj = -0.556508, rho = -0.021220
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.332184
obj = -0.671052, rho = -0.048333
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.274603
obj = -0.820644, rho = -0.093887
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.236681
obj = -1.009553, rho = -0.132362
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.201724
obj = -1.238679, rho = -0.166484
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.169843
obj = -1.519650, rho = -0.181270
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.144610
obj = -1.880429, rho = -0.234403
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.124098
obj = -2.341247, rho = -0.250847
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.105241
obj = -2.934854, rho = -0.207380
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.093313
obj = -3.712163, rho = -0.075920
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.087129
obj = -4.574963, rho = 0.217795
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.078726
obj = -5.383778, rho = 0.380558
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.063747
obj = -6.069743, rho = 0.470899
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
....*...*
optimization finished, #iter = 796
nu = 0.048722
obj = -6.759202, rho = 0.553364
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 604
nu = 0.041776
obj = -7.320990, rho = 0.873892
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*...*
optimization finished, #iter = 455
nu = 0.030373
obj = -7.338778, rho = 0.871320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*...*
optimization finished, #iter = 455
nu = 0.021115
obj = -7.338778, rho = 0.871320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*...*
optimization finished, #iter = 455
nu = 0.014679
obj = -7.338778, rho = 0.871320
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.597133
obj = -0.406575, rho = -0.187659
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.496057
obj = -0.495073, rho = -0.242178
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.430303
obj = -0.600494, rho = -0.219770
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.359424
obj = -0.724664, rho = -0.179060
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.310627
obj = -0.865778, rho = -0.125674
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.253589
obj = -1.028586, rho = -0.225553
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.210006
obj = -1.222651, rho = -0.230097
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.173258
obj = -1.463349, rho = -0.222539
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.146811
obj = -1.745511, rho = -0.280104
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.129294
obj = -2.026196, rho = -0.516560
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*.*
optimization finished, #iter = 408
nu = 0.108768
obj = -2.155594, rho = -0.836411
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.......*.*
optimization finished, #iter = 863
nu = 0.078381
obj = -2.212331, rho = -0.857306
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*....*
optimization finished, #iter = 801
nu = 0.055404
obj = -2.274497, rho = -0.849860
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.041070
obj = -2.349304, rho = -0.817699
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.........*
optimization finished, #iter = 1174
nu = 0.028954
obj = -2.350535, rho = -0.809578
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.........*
optimization finished, #iter = 1174
nu = 0.020128
obj = -2.350535, rho = -0.809578
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.........*
optimization finished, #iter = 1174
nu = 0.013993
obj = -2.350535, rho = -0.809578
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.........*
optimization finished, #iter = 1174
nu = 0.009728
obj = -2.350535, rho = -0.809578
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.........*
optimization finished, #iter = 1174
nu = 0.006763
obj = -2.350535, rho = -0.809578
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.........*
optimization finished, #iter = 1174
nu = 0.004701
obj = -2.350535, rho = -0.809578
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.580000
obj = -0.371744, rho = -0.111894
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.473806
obj = -0.435821, rho = -0.117133
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.388508
obj = -0.510123, rho = -0.098074
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.317772
obj = -0.597623, rho = -0.111695
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.261296
obj = -0.697793, rho = -0.143071
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.209484
obj = -0.809033, rho = -0.136975
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.168919
obj = -0.942196, rho = -0.147431
nSV = 20, nBSV = 16
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.143557
obj = -1.076973, rho = -0.189429
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.111352
obj = -1.205012, rho = -0.233194
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 181
nu = 0.088363
obj = -1.332590, rho = -0.210799
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.070172
obj = -1.439075, rho = -0.313199
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.052467
obj = -1.510229, rho = -0.444375
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.038142
obj = -1.568189, rho = -0.509832
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.028126
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.019553
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.013593
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.009450
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.006569
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.004567
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*............*
optimization finished, #iter = 1320
nu = 0.003175
obj = -1.587923, rho = -0.645610
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.586582
obj = -0.405020, rho = -0.047303
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.511089
obj = -0.493411, rho = -0.036537
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.429229
obj = -0.595697, rho = -0.119143
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.371191
obj = -0.707014, rho = -0.114251
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.308374
obj = -0.834335, rho = -0.079083
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.252269
obj = -0.970419, rho = -0.033107
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.205946
obj = -1.117637, rho = 0.064150
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.164043
obj = -1.279082, rho = 0.052424
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.126703
obj = -1.473775, rho = 0.033775
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.106328
obj = -1.710571, rho = 0.063353
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.086893
obj = -1.914061, rho = 0.005258
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.069199
obj = -2.065957, rho = 0.114834
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.051642
obj = -2.190184, rho = 0.013027
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.039833
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.027692
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.019251
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.013383
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.009304
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.006468
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.004497
obj = -2.248497, rho = -0.182723
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.603512
obj = -0.414942, rho = -0.274019
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.512031
obj = -0.504464, rho = -0.242865
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.439190
obj = -0.609074, rho = -0.208144
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.361445
obj = -0.734318, rho = -0.215705
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.306274
obj = -0.890939, rho = -0.281412
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.259771
obj = -1.077060, rho = -0.297606
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.214406
obj = -1.301645, rho = -0.291912
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.180690
obj = -1.593627, rho = -0.255577
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.152638
obj = -1.948099, rho = -0.238845
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.129981
obj = -2.389091, rho = -0.289100
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 286
nu = 0.110365
obj = -2.945518, rho = -0.340648
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 189
nu = 0.097211
obj = -3.581927, rho = -0.280868
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.083069
obj = -4.313170, rho = -0.453454
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 248
nu = 0.070179
obj = -5.168045, rho = -0.433179
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*.........*
optimization finished, #iter = 1262
nu = 0.060964
obj = -5.943361, rho = -0.555446
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*....*
optimization finished, #iter = 571
nu = 0.048649
obj = -6.684501, rho = -0.683693
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*..*
optimization finished, #iter = 523
nu = 0.041449
obj = -7.190169, rho = -0.894935
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.029972
obj = -7.242387, rho = -0.913300
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.020836
obj = -7.242387, rho = -0.913300
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.014485
obj = -7.242387, rho = -0.913300
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.597505
obj = -0.400306, rho = -0.025617
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.500000
obj = -0.483207, rho = 0.013888
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.424782
obj = -0.576517, rho = -0.009675
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.351358
obj = -0.684666, rho = -0.058402
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.288642
obj = -0.817284, rho = -0.038836
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.244982
obj = -0.968964, rho = -0.040093
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.194597
obj = -1.151149, rho = -0.074399
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.167263
obj = -1.375952, rho = -0.277260
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 303
nu = 0.139891
obj = -1.599151, rho = -0.404085
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.110410
obj = -1.867000, rho = -0.308831
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.087311
obj = -2.211251, rho = -0.276951
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*...*
optimization finished, #iter = 503
nu = 0.072588
obj = -2.644440, rho = -0.247966
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*......*
optimization finished, #iter = 985
nu = 0.062593
obj = -3.145249, rho = -0.019477
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.049550
obj = -3.706218, rho = 0.027220
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.041700
obj = -4.408740, rho = -0.034398
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.036070
obj = -5.197151, rho = -0.042213
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*....*
optimization finished, #iter = 564
nu = 0.033015
obj = -5.616360, rho = -0.040481
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93% (930/1000) (classification)
...*......*
optimization finished, #iter = 935
nu = 0.023255
obj = -5.619721, rho = -0.075753
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
...*......*
optimization finished, #iter = 935
nu = 0.016166
obj = -5.619721, rho = -0.075753
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
...*......*
optimization finished, #iter = 935
nu = 0.011239
obj = -5.619721, rho = -0.075753
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.598813
obj = -0.403024, rho = -0.014046
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.505643
obj = -0.487231, rho = -0.048101
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.425576
obj = -0.583218, rho = 0.041524
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.357067
obj = -0.695394, rho = 0.013393
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.287825
obj = -0.832887, rho = 0.030281
nSV = 34, nBSV = 22
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.239229
obj = -1.013964, rho = 0.060349
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.207057
obj = -1.233963, rho = 0.034543
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.175045
obj = -1.489500, rho = 0.123338
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.149792
obj = -1.778297, rho = -0.007334
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.122627
obj = -2.080867, rho = -0.111136
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.100205
obj = -2.440180, rho = -0.000379
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.084534
obj = -2.856398, rho = 0.405319
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.065965
obj = -3.306624, rho = 0.365225
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 348
nu = 0.055117
obj = -3.871220, rho = 0.346039
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.045051
obj = -4.366725, rho = 0.331902
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.034787
obj = -4.957232, rho = 0.234487
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.030186
obj = -5.545104, rho = 0.103722
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.023189
obj = -5.605275, rho = 0.055907
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.016121
obj = -5.605275, rho = 0.055907
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.011207
obj = -5.605275, rho = 0.055907
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.601090
obj = -0.401400, rho = -0.119778
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.510887
obj = -0.480394, rho = -0.135805
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.430220
obj = -0.564559, rho = -0.153970
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.348761
obj = -0.657098, rho = -0.123605
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.293411
obj = -0.759371, rho = -0.067656
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.231480
obj = -0.860610, rho = -0.051890
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.184630
obj = -0.967455, rho = -0.076714
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.148320
obj = -1.077164, rho = -0.138695
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.112061
obj = -1.181465, rho = -0.151881
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.083614
obj = -1.311069, rho = -0.151948
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 240
nu = 0.067130
obj = -1.466895, rho = -0.148188
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.053807
obj = -1.590046, rho = -0.006097
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.041063
obj = -1.641792, rho = 0.058306
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.029126
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.020248
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.014076
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.009786
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.006803
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.004729
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.003288
obj = -1.643928, rho = 0.062803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.598423
obj = -0.405434, rho = -0.121664
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.497582
obj = -0.492328, rho = -0.167323
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.420000
obj = -0.600202, rho = -0.204068
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.360805
obj = -0.723974, rho = -0.175713
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.302248
obj = -0.874218, rho = -0.270260
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.252454
obj = -1.055868, rho = -0.388911
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.209708
obj = -1.285962, rho = -0.405945
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.177128
obj = -1.570715, rho = -0.439694
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.153512
obj = -1.925947, rho = -0.544914
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.125855
obj = -2.358622, rho = -0.584993
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.107197
obj = -2.932428, rho = -0.565949
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.092590
obj = -3.666861, rho = -0.552137
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.081829
obj = -4.580076, rho = -0.619411
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.069638
obj = -5.732157, rho = -0.817160
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.060167
obj = -7.224242, rho = -0.994082
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.051553
obj = -9.226911, rho = -1.065943
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.047988
obj = -11.822064, rho = -1.253589
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 390
nu = 0.045770
obj = -14.464969, rho = -1.591475
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*.*
optimization finished, #iter = 480
nu = 0.036527
obj = -17.298449, rho = -1.644260
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.......*
optimization finished, #iter = 756
nu = 0.031962
obj = -20.998152, rho = -1.705650
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.572039
obj = -0.385000, rho = -0.316421
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.486420
obj = -0.462249, rho = -0.320427
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.401208
obj = -0.551917, rho = -0.292917
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.334131
obj = -0.658098, rho = -0.278792
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.278568
obj = -0.785081, rho = -0.285554
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.225574
obj = -0.943235, rho = -0.298296
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.190792
obj = -1.132357, rho = -0.210723
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 279
nu = 0.155307
obj = -1.373375, rho = -0.172652
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.129985
obj = -1.692577, rho = -0.247542
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.111442
obj = -2.103813, rho = -0.255652
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.095721
obj = -2.613607, rho = -0.311534
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.082442
obj = -3.268440, rho = -0.337436
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.071037
obj = -4.133675, rho = -0.339112
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.062355
obj = -5.221562, rho = -0.267817
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.056768
obj = -6.608830, rho = -0.190803
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.047881
obj = -8.296097, rho = -0.323157
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.041643
obj = -10.564568, rho = -0.381431
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.035844
obj = -13.649137, rho = -0.354467
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.032882
obj = -17.856546, rho = -0.399518
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 91.9% (919/1000) (classification)
.*..*
optimization finished, #iter = 321
nu = 0.030784
obj = -22.818782, rho = -0.501478
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 89.4% (894/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.606863
obj = -0.422417, rho = -0.165292
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.535240
obj = -0.514328, rho = -0.185620
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.440103
obj = -0.618694, rho = -0.209030
nSV = 50, nBSV = 41
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.367594
obj = -0.749539, rho = -0.304198
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.310620
obj = -0.914738, rho = -0.280979
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.263354
obj = -1.113691, rho = -0.313619
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.221597
obj = -1.357304, rho = -0.303104
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.193052
obj = -1.646689, rho = -0.259513
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 492
nu = 0.157002
obj = -1.997936, rho = -0.257625
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.133697
obj = -2.470250, rho = -0.308353
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.117327
obj = -3.008547, rho = -0.261700
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.101346
obj = -3.608198, rho = -0.055215
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.088816
obj = -4.165709, rho = -0.261795
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 281
nu = 0.067994
obj = -4.718727, rho = -0.329848
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 307
nu = 0.052718
obj = -5.466598, rho = -0.356191
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.045485
obj = -6.288339, rho = -0.212304
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.036988
obj = -7.025369, rho = -0.320776
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.029697
obj = -7.177010, rho = -0.323602
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.020645
obj = -7.177010, rho = -0.323602
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.014352
obj = -7.177010, rho = -0.323602
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.618970
obj = -0.421633, rho = -0.072406
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.524480
obj = -0.511719, rho = -0.073417
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.446042
obj = -0.618980, rho = -0.081736
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.370725
obj = -0.745575, rho = -0.063295
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.317338
obj = -0.896429, rho = -0.028745
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.258060
obj = -1.068040, rho = -0.023427
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.220000
obj = -1.288374, rho = 0.108252
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.183861
obj = -1.520876, rho = 0.077104
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.154778
obj = -1.778502, rho = 0.075379
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.123537
obj = -2.070669, rho = 0.065086
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 235
nu = 0.101741
obj = -2.389373, rho = 0.015896
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 257
nu = 0.078369
obj = -2.785506, rho = 0.041486
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.065098
obj = -3.304533, rho = 0.027339
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.057396
obj = -3.779413, rho = -0.054651
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 219
nu = 0.047378
obj = -4.023228, rho = -0.046819
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.034618
obj = -4.043151, rho = -0.094741
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.024066
obj = -4.043151, rho = -0.094741
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.016730
obj = -4.043151, rho = -0.094741
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.011631
obj = -4.043151, rho = -0.094741
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.008086
obj = -4.043151, rho = -0.094741
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.564029
obj = -0.380180, rho = 0.017910
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.474396
obj = -0.458565, rho = 0.026569
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.398108
obj = -0.550408, rho = 0.109045
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.336750
obj = -0.658832, rho = 0.202292
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.276698
obj = -0.784700, rho = 0.213720
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.229731
obj = -0.942189, rho = 0.293880
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.191473
obj = -1.131074, rho = 0.361527
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.158227
obj = -1.357080, rho = 0.403839
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.133727
obj = -1.632596, rho = 0.424077
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.109643
obj = -1.963527, rho = 0.409161
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
....*..*
optimization finished, #iter = 644
nu = 0.090334
obj = -2.401794, rho = 0.360157
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 381
nu = 0.074038
obj = -3.004485, rho = 0.358958
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*...*
optimization finished, #iter = 432
nu = 0.063217
obj = -3.854360, rho = 0.355314
nSV = 16, nBSV = 4
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.057236
obj = -4.993788, rho = 0.317637
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 389
nu = 0.052821
obj = -6.392015, rho = 0.267827
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.046927
obj = -8.147986, rho = 0.155997
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.042122
obj = -10.204885, rho = 0.159218
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.036879
obj = -12.709857, rho = 0.410210
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.032933
obj = -15.657120, rho = 0.723556
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
...*..*.*
optimization finished, #iter = 648
nu = 0.030888
obj = -18.398846, rho = 1.551969
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 91.8% (918/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.626751
obj = -0.435544, rho = -0.028445
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.540000
obj = -0.535140, rho = -0.045502
nSV = 54, nBSV = 54
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.461593
obj = -0.650320, rho = -0.088082
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.389492
obj = -0.787470, rho = -0.127352
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.330694
obj = -0.958278, rho = -0.176758
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.282918
obj = -1.150083, rho = -0.176702
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.231834
obj = -1.379081, rho = -0.238581
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.195300
obj = -1.670533, rho = -0.058240
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.171239
obj = -1.972729, rho = -0.340112
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.137716
obj = -2.290766, rho = -0.352870
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.115745
obj = -2.618509, rho = -0.232686
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.097034
obj = -2.876132, rho = -0.100849
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*
optimization finished, #iter = 283
nu = 0.070871
obj = -3.047859, rho = -0.098842
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.053531
obj = -3.193558, rho = -0.147030
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.040222
obj = -3.265946, rho = -0.140950
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.027962
obj = -3.265946, rho = -0.140950
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.019439
obj = -3.265946, rho = -0.140950
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.013514
obj = -3.265946, rho = -0.140950
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.009395
obj = -3.265946, rho = -0.140950
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.006531
obj = -3.265946, rho = -0.140950
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.593745
obj = -0.396501, rho = -0.318534
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.504563
obj = -0.472418, rho = -0.299543
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.409145
obj = -0.562064, rho = -0.323937
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.345874
obj = -0.668218, rho = -0.240830
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.280264
obj = -0.792562, rho = -0.278497
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.229012
obj = -0.944346, rho = -0.253898
nSV = 30, nBSV = 19
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.193338
obj = -1.138180, rho = -0.297028
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.164763
obj = -1.347500, rho = -0.217441
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.134196
obj = -1.585842, rho = -0.230922
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.112483
obj = -1.825961, rho = -0.139034
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.094424
obj = -2.060698, rho = -0.290103
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.075448
obj = -2.231119, rho = -0.589769
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*
optimization finished, #iter = 328
nu = 0.057785
obj = -2.298495, rho = -0.680194
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.040921
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.028448
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.019777
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.013749
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.009558
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.006645
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.004619
obj = -2.309980, rho = -0.658145
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.653627
obj = -0.448494, rho = -0.108502
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.575179
obj = -0.537084, rho = -0.059527
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.480000
obj = -0.634191, rho = -0.088661
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.388061
obj = -0.741053, rho = -0.052992
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.320770
obj = -0.861766, rho = 0.044627
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.259361
obj = -0.995331, rho = 0.041682
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.206517
obj = -1.151793, rho = 0.018779
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.169342
obj = -1.329434, rho = 0.061905
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.135381
obj = -1.536246, rho = -0.011065
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*.*
optimization finished, #iter = 399
nu = 0.107138
obj = -1.772645, rho = -0.015957
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*....*
optimization finished, #iter = 603
nu = 0.086382
obj = -2.049094, rho = -0.033347
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 287
nu = 0.072979
obj = -2.337328, rho = -0.129362
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.056089
obj = -2.575047, rho = -0.101629
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.043215
obj = -2.879036, rho = -0.105274
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.037866
obj = -3.079163, rho = -0.187541
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.026369
obj = -3.079171, rho = -0.187568
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.018331
obj = -3.079171, rho = -0.187568
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.012744
obj = -3.079171, rho = -0.187568
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.008859
obj = -3.079171, rho = -0.187568
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.006159
obj = -3.079171, rho = -0.187568
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.610273
obj = -0.417996, rho = -0.088147
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.526823
obj = -0.503969, rho = -0.037645
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.440698
obj = -0.602412, rho = 0.015571
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.360397
obj = -0.725018, rho = 0.014221
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.294757
obj = -0.883912, rho = 0.004508
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.254485
obj = -1.080069, rho = 0.090263
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.219125
obj = -1.319385, rho = 0.183730
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.184121
obj = -1.590186, rho = 0.171682
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.151635
obj = -1.936752, rho = 0.141184
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.126825
obj = -2.394561, rho = 0.231002
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.112158
obj = -2.957022, rho = 0.292570
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.095158
obj = -3.604883, rho = 0.202407
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.080540
obj = -4.450045, rho = 0.226862
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.072025
obj = -5.415677, rho = 0.257012
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.....*
optimization finished, #iter = 633
nu = 0.065655
obj = -6.343889, rho = 0.083892
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.055817
obj = -6.897590, rho = -0.432865
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.041273
obj = -6.934099, rho = -0.626721
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.028693
obj = -6.934099, rho = -0.626721
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.019947
obj = -6.934099, rho = -0.626721
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.013867
obj = -6.934099, rho = -0.626721
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.598574
obj = -0.401162, rho = -0.177523
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.498697
obj = -0.482367, rho = -0.161656
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.416380
obj = -0.580876, rho = -0.240834
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.347838
obj = -0.698413, rho = -0.149861
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.287395
obj = -0.849839, rho = -0.137084
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.249854
obj = -1.032798, rho = -0.241194
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.210468
obj = -1.231624, rho = -0.342044
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.177460
obj = -1.455616, rho = -0.414161
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.148457
obj = -1.705542, rho = -0.363671
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.122179
obj = -1.969574, rho = -0.406255
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.098297
obj = -2.239743, rho = -0.385371
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*..*.*
optimization finished, #iter = 464
nu = 0.079073
obj = -2.457438, rho = -0.322655
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.058558
obj = -2.711383, rho = -0.365157
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.044938
obj = -3.057406, rho = -0.380138
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.037398
obj = -3.416431, rho = -0.392663
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.030435
obj = -3.614502, rho = -0.493028
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.021526
obj = -3.616512, rho = -0.509826
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.014965
obj = -3.616512, rho = -0.509826
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.010403
obj = -3.616512, rho = -0.509826
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.007232
obj = -3.616512, rho = -0.509826
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.605435
obj = -0.425928, rho = -0.054094
nSV = 66, nBSV = 57
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.525427
obj = -0.524052, rho = -0.012318
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.446230
obj = -0.642564, rho = 0.005438
nSV = 46, nBSV = 44
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.384986
obj = -0.783643, rho = 0.072303
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.330787
obj = -0.948902, rho = 0.071562
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.276297
obj = -1.142533, rho = 0.141207
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.234172
obj = -1.373344, rho = 0.123016
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.201181
obj = -1.623136, rho = 0.102131
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.163946
obj = -1.882856, rho = 0.006926
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.130807
obj = -2.179463, rho = -0.012344
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 197
nu = 0.106381
obj = -2.531609, rho = -0.078378
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.086635
obj = -2.940025, rho = -0.083924
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.075024
obj = -3.305173, rho = -0.182946
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.056012
obj = -3.568361, rho = -0.211842
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.043515
obj = -3.760588, rho = -0.171940
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.032744
obj = -3.903174, rho = -0.270073
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.023260
obj = -3.908358, rho = -0.303249
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.016170
obj = -3.908358, rho = -0.303249
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.011241
obj = -3.908358, rho = -0.303249
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.007815
obj = -3.908358, rho = -0.303249
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.560000
obj = -0.384922, rho = -0.176701
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.481511
obj = -0.466856, rho = -0.104587
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.406579
obj = -0.564674, rho = -0.047338
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.344008
obj = -0.674943, rho = 0.046812
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.285029
obj = -0.806693, rho = 0.015807
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.245297
obj = -0.957472, rho = 0.047110
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.199103
obj = -1.109964, rho = 0.012665
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.157615
obj = -1.294320, rho = 0.004898
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.126327
obj = -1.542268, rho = 0.034954
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.111766
obj = -1.816221, rho = 0.022495
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.091289
obj = -2.035788, rho = 0.015601
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.073290
obj = -2.244269, rho = 0.073265
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.057258
obj = -2.421569, rho = 0.083075
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.044121
obj = -2.491812, rho = 0.125778
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.030694
obj = -2.491819, rho = 0.129418
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.021338
obj = -2.491819, rho = 0.129418
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.014834
obj = -2.491819, rho = 0.129418
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.010312
obj = -2.491819, rho = 0.129418
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.007169
obj = -2.491819, rho = 0.129418
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.004984
obj = -2.491819, rho = 0.129418
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.587562
obj = -0.407492, rho = -0.267540
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.511797
obj = -0.494531, rho = -0.267442
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.429209
obj = -0.597755, rho = -0.352108
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.366551
obj = -0.711353, rho = -0.476465
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.303316
obj = -0.843875, rho = -0.452247
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.248943
obj = -0.993431, rho = -0.501683
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.203044
obj = -1.181144, rho = -0.437577
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.171755
obj = -1.391680, rho = -0.412984
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 362
nu = 0.135737
obj = -1.631802, rho = -0.417082
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.108936
obj = -1.957138, rho = -0.390668
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.092487
obj = -2.387442, rho = -0.398431
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.077618
obj = -2.908155, rho = -0.472532
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.066991
obj = -3.514962, rho = -0.547840
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.055857
obj = -4.208667, rho = -0.745838
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.048065
obj = -4.978365, rho = -1.061421
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.039100
obj = -5.848980, rho = -1.295558
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.033478
obj = -6.766195, rho = -1.492989
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.029951
obj = -7.239726, rho = -1.914739
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.020821
obj = -7.239726, rho = -1.914739
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.014475
obj = -7.239726, rho = -1.914739
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.667257
obj = -0.459966, rho = -0.217117
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.569423
obj = -0.562377, rho = -0.226172
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.493598
obj = -0.681713, rho = -0.151358
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.408171
obj = -0.817827, rho = -0.151084
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.344373
obj = -0.987829, rho = -0.217818
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.286841
obj = -1.190332, rho = -0.215216
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.236079
obj = -1.443705, rho = -0.204167
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 346
nu = 0.201051
obj = -1.758395, rho = -0.224685
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 238
nu = 0.175548
obj = -2.127163, rho = -0.236901
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*
optimization finished, #iter = 456
nu = 0.143964
obj = -2.550439, rho = -0.267315
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.117916
obj = -3.099775, rho = -0.227075
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.102200
obj = -3.750577, rho = -0.137311
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.085963
obj = -4.504761, rho = -0.063801
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.073147
obj = -5.363262, rho = -0.073300
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.063561
obj = -6.226914, rho = -0.086144
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.050217
obj = -6.908265, rho = 0.005921
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
...*.*
optimization finished, #iter = 494
nu = 0.039467
obj = -7.710514, rho = 0.110897
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.033560
obj = -8.111486, rho = 0.204487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.023330
obj = -8.111486, rho = 0.204487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.016219
obj = -8.111486, rho = 0.204487
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.574761
obj = -0.385421, rho = -0.136809
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.486941
obj = -0.460799, rho = -0.076053
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.416677
obj = -0.542822, rho = -0.138000
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.332533
obj = -0.629723, rho = -0.135672
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.265454
obj = -0.741050, rho = -0.174207
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.219947
obj = -0.872913, rho = -0.312536
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.178075
obj = -1.023608, rho = -0.282550
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.149303
obj = -1.204684, rho = -0.185428
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.118293
obj = -1.411113, rho = -0.190182
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.095009
obj = -1.682924, rho = -0.209431
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.081124
obj = -2.025074, rho = -0.165955
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.070218
obj = -2.350356, rho = -0.054955
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.057626
obj = -2.659384, rho = 0.025170
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.046565
obj = -2.946209, rho = 0.126666
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.037335
obj = -3.032043, rho = 0.250589
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.025955
obj = -3.032043, rho = 0.250589
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.018044
obj = -3.032043, rho = 0.250589
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.012544
obj = -3.032043, rho = 0.250589
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.008720
obj = -3.032043, rho = 0.250589
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.006062
obj = -3.032043, rho = 0.250589
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.619465
obj = -0.422809, rho = -0.031671
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.518701
obj = -0.513892, rho = 0.003462
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.449431
obj = -0.626741, rho = 0.084025
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.380575
obj = -0.750311, rho = 0.083086
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.317532
obj = -0.894050, rho = 0.056224
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.257924
obj = -1.068441, rho = 0.032261
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.219556
obj = -1.289988, rho = 0.026139
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.182173
obj = -1.541975, rho = -0.020807
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.149479
obj = -1.850013, rho = -0.058996
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.125426
obj = -2.254671, rho = -0.160706
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.111198
obj = -2.682596, rho = -0.257397
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.093181
obj = -3.109636, rho = -0.118847
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*..*
optimization finished, #iter = 573
nu = 0.076669
obj = -3.478745, rho = -0.062885
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 403
nu = 0.060219
obj = -3.825402, rho = -0.016306
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 272
nu = 0.046813
obj = -4.080624, rho = 0.059146
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.035339
obj = -4.127458, rho = 0.112356
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.024567
obj = -4.127458, rho = 0.112356
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.017079
obj = -4.127458, rho = 0.112356
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.011873
obj = -4.127458, rho = 0.112356
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.008254
obj = -4.127458, rho = 0.112356
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.610761
obj = -0.404063, rho = -0.188549
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.515157
obj = -0.478644, rho = -0.243119
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.427777
obj = -0.564615, rho = -0.207343
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.347669
obj = -0.661355, rho = -0.216699
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.277182
obj = -0.779860, rho = -0.218263
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.228226
obj = -0.931675, rho = -0.191313
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.193297
obj = -1.099304, rho = -0.105502
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.160265
obj = -1.275969, rho = -0.063747
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.128633
obj = -1.478389, rho = -0.238727
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.104245
obj = -1.709309, rho = -0.374206
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.083206
obj = -1.984042, rho = -0.406193
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.070951
obj = -2.252276, rho = -0.527882
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.058636
obj = -2.431643, rho = -0.690978
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.043727
obj = -2.474891, rho = -0.868076
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.030482
obj = -2.474969, rho = -0.867753
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.021191
obj = -2.474969, rho = -0.867753
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.014732
obj = -2.474969, rho = -0.867753
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.010242
obj = -2.474969, rho = -0.867753
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.007120
obj = -2.474969, rho = -0.867753
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.004950
obj = -2.474969, rho = -0.867753
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.605514
obj = -0.432148, rho = -0.126966
nSV = 66, nBSV = 57
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.528815
obj = -0.538826, rho = -0.130712
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.462161
obj = -0.663577, rho = -0.177203
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.393308
obj = -0.808326, rho = -0.111869
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.333413
obj = -0.991257, rho = -0.069462
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.281297
obj = -1.215685, rho = -0.034601
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.239282
obj = -1.502783, rho = 0.013912
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.207634
obj = -1.865006, rho = -0.091136
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.176070
obj = -2.305361, rho = -0.049653
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 297
nu = 0.153575
obj = -2.872006, rho = 0.067831
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.128267
obj = -3.595058, rho = 0.072889
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.111320
obj = -4.580829, rho = 0.133419
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.100324
obj = -5.834592, rho = 0.151627
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 410
nu = 0.085850
obj = -7.466022, rho = 0.134347
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 254
nu = 0.074402
obj = -9.745195, rho = 0.140346
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.071067
obj = -12.740126, rho = 0.162098
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 496
nu = 0.065406
obj = -16.234535, rho = 0.059566
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*...*
optimization finished, #iter = 520
nu = 0.057453
obj = -20.455954, rho = -0.274363
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.....*..*
optimization finished, #iter = 701
nu = 0.049158
obj = -26.030065, rho = -0.312227
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.....*..*
optimization finished, #iter = 767
nu = 0.044667
obj = -33.390676, rho = -0.314241
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.596205
obj = -0.409059, rho = -0.264170
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.498731
obj = -0.497417, rho = -0.235610
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.423288
obj = -0.606900, rho = -0.150363
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.356565
obj = -0.748089, rho = -0.138770
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.307729
obj = -0.917150, rho = -0.250643
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.257193
obj = -1.132609, rho = -0.287542
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.223173
obj = -1.407834, rho = -0.325083
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.193439
obj = -1.743795, rho = -0.422605
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.166987
obj = -2.148568, rho = -0.511275
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.145668
obj = -2.632125, rho = -0.581770
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 356
nu = 0.126223
obj = -3.153841, rho = -0.476617
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.101861
obj = -3.805517, rho = -0.556747
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.083524
obj = -4.662600, rho = -0.611078
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.071909
obj = -5.792223, rho = -0.556983
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 284
nu = 0.063700
obj = -7.180313, rho = -0.534570
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.058555
obj = -8.564873, rho = -0.638183
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*
optimization finished, #iter = 385
nu = 0.053364
obj = -9.518641, rho = -0.906914
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*..*
optimization finished, #iter = 541
nu = 0.039800
obj = -9.618771, rho = -1.093627
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*..*
optimization finished, #iter = 541
nu = 0.027669
obj = -9.618771, rho = -1.093627
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*..*
optimization finished, #iter = 541
nu = 0.019235
obj = -9.618771, rho = -1.093627
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.592501
obj = -0.409899, rho = -0.102225
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.499145
obj = -0.502390, rho = -0.093527
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.425008
obj = -0.618308, rho = -0.081600
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.363255
obj = -0.763995, rho = -0.128045
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.317352
obj = -0.942333, rho = -0.212234
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.272050
obj = -1.149797, rho = -0.225220
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.225952
obj = -1.410878, rho = -0.189885
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.189258
obj = -1.749533, rho = -0.164964
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*..*
optimization finished, #iter = 307
nu = 0.162493
obj = -2.203623, rho = -0.111878
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.141263
obj = -2.809807, rho = -0.133320
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.126515
obj = -3.591646, rho = -0.185355
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.114481
obj = -4.512933, rho = -0.256895
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.101849
obj = -5.623894, rho = -0.361953
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.094398
obj = -6.815088, rho = -0.677090
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.077037
obj = -7.853956, rho = -0.500837
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.067395
obj = -8.860623, rho = -0.601195
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
......*
optimization finished, #iter = 642
nu = 0.054280
obj = -9.117703, rho = -0.721574
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
......*
optimization finished, #iter = 642
nu = 0.037735
obj = -9.117703, rho = -0.721574
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
......*
optimization finished, #iter = 642
nu = 0.026233
obj = -9.117703, rho = -0.721574
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
......*
optimization finished, #iter = 642
nu = 0.018237
obj = -9.117703, rho = -0.721574
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.582366
obj = -0.380201, rho = -0.273815
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.488752
obj = -0.445513, rho = -0.296314
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.388880
obj = -0.523896, rho = -0.284912
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.326059
obj = -0.612803, rho = -0.247252
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.268294
obj = -0.706997, rho = -0.181179
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.216622
obj = -0.812984, rho = -0.261329
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.171543
obj = -0.927595, rho = -0.369961
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.131955
obj = -1.070220, rho = -0.369315
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.108375
obj = -1.245140, rho = -0.319451
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.090046
obj = -1.423944, rho = -0.282033
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.072829
obj = -1.599552, rho = -0.491200
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.056796
obj = -1.767782, rho = -0.618444
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.043406
obj = -1.912489, rho = -0.711500
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.033432
obj = -2.065190, rho = -0.747173
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.025917
obj = -2.104488, rho = -0.581837
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.018018
obj = -2.104488, rho = -0.581837
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.012526
obj = -2.104488, rho = -0.581837
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.008708
obj = -2.104488, rho = -0.581837
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.006054
obj = -2.104488, rho = -0.581837
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.004208
obj = -2.104488, rho = -0.581837
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.612442
obj = -0.418490, rho = -0.178975
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.520764
obj = -0.505578, rho = -0.144443
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.439208
obj = -0.609525, rho = -0.137617
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.367700
obj = -0.730856, rho = -0.137050
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.308598
obj = -0.874799, rho = -0.144917
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.253107
obj = -1.051251, rho = -0.199908
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.212883
obj = -1.259410, rho = -0.209732
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 273
nu = 0.170504
obj = -1.535657, rho = -0.215149
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.146485
obj = -1.909541, rho = -0.278249
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.134523
obj = -2.319524, rho = -0.491845
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.111451
obj = -2.761002, rho = -0.560740
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.098503
obj = -3.185776, rho = -0.630636
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*........*
optimization finished, #iter = 1216
nu = 0.078438
obj = -3.512894, rho = -0.623523
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.062167
obj = -3.782026, rho = -0.721519
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*..*
optimization finished, #iter = 543
nu = 0.047375
obj = -3.848118, rho = -0.813363
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*...*
optimization finished, #iter = 620
nu = 0.032948
obj = -3.848130, rho = -0.812799
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*...*
optimization finished, #iter = 620
nu = 0.022905
obj = -3.848130, rho = -0.812799
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*...*
optimization finished, #iter = 620
nu = 0.015923
obj = -3.848130, rho = -0.812799
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*...*
optimization finished, #iter = 620
nu = 0.011070
obj = -3.848130, rho = -0.812799
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*...*
optimization finished, #iter = 620
nu = 0.007696
obj = -3.848130, rho = -0.812799
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.609341
obj = -0.402631, rho = -0.138304
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.503685
obj = -0.479170, rho = -0.124646
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.420532
obj = -0.568363, rho = -0.154211
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.338726
obj = -0.679818, rho = -0.172658
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.291730
obj = -0.817810, rho = -0.126615
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.238829
obj = -0.971931, rho = -0.137018
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.193663
obj = -1.172970, rho = -0.139884
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.165427
obj = -1.424714, rho = -0.114692
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.134185
obj = -1.740202, rho = -0.113423
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.120619
obj = -2.125520, rho = -0.012772
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.099493
obj = -2.550619, rho = -0.027237
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.083895
obj = -3.085963, rho = 0.003650
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.072853
obj = -3.673233, rho = -0.050404
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.059746
obj = -4.317529, rho = -0.064202
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.048713
obj = -5.027456, rho = -0.095906
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.039194
obj = -5.934693, rho = -0.086616
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.034438
obj = -6.855775, rho = 0.195165
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 371
nu = 0.030213
obj = -7.301073, rho = 0.642731
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*.*
optimization finished, #iter = 371
nu = 0.021004
obj = -7.301073, rho = 0.642731
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*.*
optimization finished, #iter = 371
nu = 0.014602
obj = -7.301073, rho = 0.642731
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.603391
obj = -0.416589, rho = -0.097926
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.520000
obj = -0.509136, rho = -0.108880
nSV = 52, nBSV = 52
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.431782
obj = -0.617879, rho = -0.130882
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.362405
obj = -0.755792, rho = -0.130823
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.308099
obj = -0.930785, rho = -0.159745
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.264656
obj = -1.145300, rho = -0.126931
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.228642
obj = -1.404999, rho = -0.060503
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.198065
obj = -1.710893, rho = -0.119887
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.169616
obj = -2.068514, rho = -0.253339
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.141045
obj = -2.464453, rho = -0.420024
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.114499
obj = -2.955049, rho = -0.376434
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.096473
obj = -3.565733, rho = -0.297132
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.079682
obj = -4.362725, rho = -0.282883
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.073168
obj = -5.236348, rho = -0.641797
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*.*
optimization finished, #iter = 438
nu = 0.061431
obj = -5.918781, rho = -1.078732
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.047605
obj = -6.644139, rho = -1.144158
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.037203
obj = -7.514087, rho = -1.214284
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.030611
obj = -8.417137, rho = -1.297248
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 349
nu = 0.025223
obj = -8.766386, rho = -1.384935
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 349
nu = 0.017535
obj = -8.766386, rho = -1.384935
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.569635
obj = -0.378632, rho = -0.124082
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.478023
obj = -0.449195, rho = -0.101918
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.399877
obj = -0.530830, rho = -0.110397
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.328920
obj = -0.618840, rho = -0.095333
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.265199
obj = -0.716925, rho = -0.095782
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.214832
obj = -0.834141, rho = -0.082366
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.177967
obj = -0.958777, rho = -0.030159
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.142030
obj = -1.097092, rho = -0.064296
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.116845
obj = -1.222373, rho = -0.148805
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.092663
obj = -1.314353, rho = -0.190669
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.068592
obj = -1.359597, rho = -0.186735
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.050186
obj = -1.394371, rho = -0.212135
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.035643
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.024779
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.017226
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.011975
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.008325
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.005788
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.004023
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.002797
obj = -1.398696, rho = -0.247720
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.564212
obj = -0.382519, rho = -0.207710
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.479817
obj = -0.462838, rho = -0.185769
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.403939
obj = -0.556472, rho = -0.145942
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.336997
obj = -0.669071, rho = -0.063910
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.283254
obj = -0.803715, rho = -0.008184
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.237397
obj = -0.957259, rho = 0.059069
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.193016
obj = -1.144187, rho = 0.073723
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.163074
obj = -1.360217, rho = 0.170456
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.135470
obj = -1.623285, rho = 0.185533
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.111797
obj = -1.924867, rho = 0.222808
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.092352
obj = -2.274464, rho = 0.197269
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.074864
obj = -2.693936, rho = 0.130409
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.062924
obj = -3.220552, rho = 0.149091
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*..........................*
optimization finished, #iter = 2787
nu = 0.051054
obj = -3.802410, rho = 0.114136
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.041980
obj = -4.555823, rho = 0.043941
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.035001
obj = -5.489590, rho = 0.220684
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.030232
obj = -6.553251, rho = 0.490228
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.025559
obj = -7.598540, rho = 0.724124
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.023339
obj = -8.381509, rho = 1.008772
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.016779
obj = -8.391563, rho = 1.045606
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.561916
obj = -0.381028, rho = -0.126299
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.473536
obj = -0.462024, rho = -0.156932
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.401693
obj = -0.557818, rho = -0.111769
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.343919
obj = -0.665523, rho = -0.087247
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.283786
obj = -0.786085, rho = -0.076924
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.233330
obj = -0.924178, rho = 0.001258
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.196710
obj = -1.079624, rho = -0.043865
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.156057
obj = -1.239277, rho = -0.059948
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.127340
obj = -1.429142, rho = -0.004082
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.105614
obj = -1.586313, rho = 0.017187
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 269
nu = 0.081272
obj = -1.722872, rho = -0.081068
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.062311
obj = -1.843013, rho = -0.179887
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.047958
obj = -1.932500, rho = -0.167561
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 242
nu = 0.034475
obj = -1.945947, rho = -0.130654
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.023967
obj = -1.945947, rho = -0.130724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.016662
obj = -1.945947, rho = -0.130724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.011583
obj = -1.945947, rho = -0.130724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.008053
obj = -1.945947, rho = -0.130724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.005598
obj = -1.945947, rho = -0.130724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.003892
obj = -1.945947, rho = -0.130724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.601925
obj = -0.406265, rho = -0.258710
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.502713
obj = -0.491299, rho = -0.254332
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.421244
obj = -0.592538, rho = -0.302015
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.363465
obj = -0.713222, rho = -0.170094
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.298823
obj = -0.845772, rho = -0.179799
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.244102
obj = -1.013774, rho = -0.138200
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.204668
obj = -1.225864, rho = -0.173838
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.172395
obj = -1.479963, rho = -0.252415
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.155800
obj = -1.741392, rho = -0.378305
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
....*..*
optimization finished, #iter = 626
nu = 0.127255
obj = -1.943145, rho = -0.375384
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.095625
obj = -2.174093, rho = -0.380478
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.075844
obj = -2.443879, rho = -0.390586
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.059414
obj = -2.752263, rho = -0.380447
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.048714
obj = -2.968665, rho = -0.341686
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.037092
obj = -3.089886, rho = -0.644101
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.026521
obj = -3.096779, rho = -0.737778
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.018437
obj = -3.096779, rho = -0.737778
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.012817
obj = -3.096779, rho = -0.737778
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.008911
obj = -3.096779, rho = -0.737778
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.006195
obj = -3.096779, rho = -0.737778
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.660000
obj = -0.448585, rho = -0.146909
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.560477
obj = -0.540913, rho = -0.109039
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.469496
obj = -0.651070, rho = -0.082427
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.387841
obj = -0.785471, rho = -0.084675
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.325308
obj = -0.951019, rho = -0.143553
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.270491
obj = -1.162356, rho = -0.095633
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.229279
obj = -1.434383, rho = -0.076588
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.196604
obj = -1.775612, rho = -0.015505
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.166320
obj = -2.215948, rho = -0.066637
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.151120
obj = -2.750791, rho = 0.042705
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.127812
obj = -3.351811, rho = -0.141721
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.107982
obj = -4.117193, rho = -0.071618
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.098928
obj = -4.971054, rho = -0.200794
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.082125
obj = -5.835022, rho = -0.282892
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.069494
obj = -6.565505, rho = -0.274641
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.053540
obj = -7.301135, rho = -0.235230
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.045517
obj = -7.769023, rho = -0.711241
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.032153
obj = -7.771508, rho = -0.758545
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.022353
obj = -7.771508, rho = -0.758545
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.015539
obj = -7.771508, rho = -0.758545
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.609831
obj = -0.427619, rho = -0.166205
nSV = 65, nBSV = 57
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.518433
obj = -0.528779, rho = -0.154643
nSV = 52, nBSV = 50
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.455281
obj = -0.649217, rho = -0.212300
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.384220
obj = -0.797048, rho = -0.199437
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.334400
obj = -0.972479, rho = -0.358554
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.280079
obj = -1.176331, rho = -0.446568
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.238703
obj = -1.420729, rho = -0.537445
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.198129
obj = -1.713389, rho = -0.540158
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.163178
obj = -2.098329, rho = -0.535572
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.141161
obj = -2.588021, rho = -0.550699
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.124170
obj = -3.135644, rho = -0.585245
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.102218
obj = -3.796940, rho = -0.779976
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.096343
obj = -4.446707, rho = -0.996716
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.079301
obj = -4.764335, rho = -1.374804
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.057455
obj = -4.949821, rho = -1.424657
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.043107
obj = -5.034586, rho = -1.461678
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.029968
obj = -5.034586, rho = -1.461678
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.020833
obj = -5.034586, rho = -1.461678
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.014483
obj = -5.034586, rho = -1.461678
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.010069
obj = -5.034586, rho = -1.461678
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.543741
obj = -0.357844, rho = -0.002094
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.454877
obj = -0.422807, rho = 0.030684
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.379095
obj = -0.492191, rho = -0.087067
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 203
nu = 0.304225
obj = -0.573252, rho = -0.121335
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.247285
obj = -0.670009, rho = -0.104680
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.198862
obj = -0.779653, rho = -0.125018
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.161474
obj = -0.911937, rho = -0.079430
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.130549
obj = -1.066861, rho = -0.051461
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.106633
obj = -1.261787, rho = -0.130888
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.088102
obj = -1.486271, rho = -0.071679
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.072737
obj = -1.729789, rho = -0.171445
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.060225
obj = -1.989924, rho = -0.316458
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.049564
obj = -2.230055, rho = -0.347622
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.039931
obj = -2.366807, rho = -0.335820
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.029569
obj = -2.401209, rho = -0.247976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.020556
obj = -2.401209, rho = -0.247976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.014290
obj = -2.401209, rho = -0.247976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.009935
obj = -2.401209, rho = -0.247976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.006906
obj = -2.401209, rho = -0.247976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.004801
obj = -2.401209, rho = -0.247976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.605875
obj = -0.432305, rho = 0.007821
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.519801
obj = -0.539120, rho = -0.083124
nSV = 56, nBSV = 48
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.448754
obj = -0.676146, rho = -0.095338
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.412077
obj = -0.829830, rho = -0.009928
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.344234
obj = -1.004122, rho = -0.066033
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 219
nu = 0.286626
obj = -1.221953, rho = -0.135378
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.243697
obj = -1.494862, rho = -0.175055
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.209759
obj = -1.822904, rho = -0.036769
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.173420
obj = -2.219631, rho = 0.017363
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.154750
obj = -2.687147, rho = -0.096889
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.134046
obj = -3.131577, rho = -0.115311
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 361
nu = 0.105452
obj = -3.599498, rho = -0.117817
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.082615
obj = -4.226512, rho = -0.179155
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.070284
obj = -4.959725, rho = -0.166461
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.061056
obj = -5.612436, rho = 0.137961
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.049741
obj = -5.808658, rho = 0.141731
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.034580
obj = -5.808658, rho = 0.141731
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.024040
obj = -5.808658, rho = 0.141731
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.016712
obj = -5.808658, rho = 0.141731
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.011618
obj = -5.808658, rho = 0.141731
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.592717
obj = -0.400847, rho = -0.206130
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.495923
obj = -0.486766, rho = -0.167240
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.424628
obj = -0.586871, rho = -0.090385
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.358079
obj = -0.702767, rho = -0.127041
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.292010
obj = -0.844395, rho = -0.138076
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.240938
obj = -1.030277, rho = -0.184201
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.205025
obj = -1.261346, rho = -0.360914
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.175560
obj = -1.540096, rho = -0.338368
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.148709
obj = -1.887463, rho = -0.359020
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.123797
obj = -2.317325, rho = -0.393232
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.108343
obj = -2.850927, rho = -0.393594
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.094074
obj = -3.479434, rho = -0.305142
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.077167
obj = -4.219463, rho = -0.249082
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.066214
obj = -5.221253, rho = -0.187534
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.....*
optimization finished, #iter = 787
nu = 0.058828
obj = -6.265927, rho = -0.072184
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*...*
optimization finished, #iter = 624
nu = 0.052339
obj = -7.333462, rho = 0.005581
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
....*...*
optimization finished, #iter = 787
nu = 0.044713
obj = -7.877860, rho = 0.108021
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*..*
optimization finished, #iter = 526
nu = 0.032872
obj = -7.943088, rho = 0.144656
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
...*..*
optimization finished, #iter = 526
nu = 0.022852
obj = -7.943088, rho = 0.144656
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
...*..*
optimization finished, #iter = 526
nu = 0.015887
obj = -7.943088, rho = 0.144656
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.676619
obj = -0.469940, rho = -0.312466
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.579610
obj = -0.571516, rho = -0.451576
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.486694
obj = -0.698034, rho = -0.492497
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.430818
obj = -0.843498, rho = -0.471438
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.357090
obj = -1.009742, rho = -0.479879
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.294472
obj = -1.199022, rho = -0.493979
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.244368
obj = -1.417966, rho = -0.501456
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.202548
obj = -1.684311, rho = -0.476904
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.173058
obj = -1.987770, rho = -0.598259
nSV = 19, nBSV = 15
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*....*
optimization finished, #iter = 407
nu = 0.139613
obj = -2.285630, rho = -0.608526
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.109643
obj = -2.652242, rho = -0.615952
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.087034
obj = -3.139056, rho = -0.633844
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*....*
optimization finished, #iter = 424
nu = 0.070923
obj = -3.747128, rho = -0.741603
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 373
nu = 0.058718
obj = -4.545750, rho = -0.811082
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*
optimization finished, #iter = 382
nu = 0.049223
obj = -5.528887, rho = -0.839144
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.043260
obj = -6.688370, rho = -1.078682
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.035855
obj = -7.937130, rho = -1.197371
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.034289
obj = -8.997214, rho = -1.717583
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.026091
obj = -9.071151, rho = -1.896694
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.018138
obj = -9.071151, rho = -1.896694
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.601483
obj = -0.408574, rho = -0.261188
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.497758
obj = -0.497130, rho = -0.224456
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.428872
obj = -0.609511, rho = -0.161870
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.377250
obj = -0.731835, rho = -0.044297
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.308261
obj = -0.870486, rho = -0.074050
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.254572
obj = -1.041900, rho = -0.090068
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.211474
obj = -1.248669, rho = -0.069744
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.174781
obj = -1.502703, rho = -0.107198
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.147090
obj = -1.817353, rho = -0.002741
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.127881
obj = -2.167353, rho = 0.089340
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.104741
obj = -2.545945, rho = -0.025064
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.084940
obj = -3.000397, rho = -0.090764
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.070323
obj = -3.551015, rho = 0.022661
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.063689
obj = -3.992274, rho = 0.435971
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.047311
obj = -4.290134, rho = 0.647635
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.034581
obj = -4.683019, rho = 0.646624
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.029892
obj = -5.022408, rho = 1.186464
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.020781
obj = -5.022408, rho = 1.186464
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.014446
obj = -5.022408, rho = 1.186464
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.010043
obj = -5.022408, rho = 1.186464
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.559229
obj = -0.366330, rho = -0.141526
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.453209
obj = -0.437389, rho = -0.171516
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.380351
obj = -0.524756, rho = -0.147779
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.325549
obj = -0.624600, rho = -0.235757
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.264325
obj = -0.739326, rho = -0.253665
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.216173
obj = -0.876180, rho = -0.216339
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.180718
obj = -1.034755, rho = -0.234351
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.145237
obj = -1.233557, rho = -0.286991
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.118214
obj = -1.490244, rho = -0.241846
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.103427
obj = -1.823726, rho = -0.134099
nSV = 13, nBSV = 9
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.090993
obj = -2.131341, rho = -0.141188
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.070884
obj = -2.472450, rho = -0.111608
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.060312
obj = -2.858950, rho = -0.361726
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.052834
obj = -3.069772, rho = -0.927405
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.037865
obj = -3.074947, rho = -0.999445
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.026323
obj = -3.074947, rho = -0.999445
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.018300
obj = -3.074947, rho = -0.999445
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.012722
obj = -3.074947, rho = -0.999445
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.008844
obj = -3.074947, rho = -0.999445
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.006148
obj = -3.074947, rho = -0.999445
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.582628
obj = -0.392312, rho = -0.322399
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.491749
obj = -0.474188, rho = -0.324922
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.409503
obj = -0.568883, rho = -0.313422
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.340372
obj = -0.686575, rho = -0.298865
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.280000
obj = -0.836541, rho = -0.327832
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.240616
obj = -1.028663, rho = -0.309336
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.200785
obj = -1.273691, rho = -0.292513
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.173496
obj = -1.573612, rho = -0.309576
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.150403
obj = -1.948954, rho = -0.331841
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.127743
obj = -2.412960, rho = -0.336596
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.110761
obj = -3.009876, rho = -0.487913
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.096130
obj = -3.727055, rho = -0.617172
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.080219
obj = -4.669906, rho = -0.560337
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.069957
obj = -5.969471, rho = -0.650421
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.066286
obj = -7.501709, rho = -0.891243
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.057696
obj = -9.119155, rho = -1.042709
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.051039
obj = -10.881998, rho = -1.178034
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.043144
obj = -12.362184, rho = -1.281180
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
..*..*
optimization finished, #iter = 429
nu = 0.034746
obj = -13.557510, rho = -1.321114
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
...*..*
optimization finished, #iter = 521
nu = 0.025468
obj = -14.850062, rho = -1.321131
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.626529
obj = -0.425986, rho = -0.050046
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.539213
obj = -0.514880, rho = -0.039214
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.453414
obj = -0.616531, rho = 0.038114
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.378843
obj = -0.727576, rho = 0.013469
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.308060
obj = -0.857948, rho = 0.030572
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.256621
obj = -1.011214, rho = 0.009650
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.207569
obj = -1.177231, rho = 0.025638
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.165084
obj = -1.394153, rho = 0.037432
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.137095
obj = -1.671441, rho = 0.042009
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.118124
obj = -1.975876, rho = 0.079983
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.099697
obj = -2.273184, rho = 0.200036
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.079497
obj = -2.534146, rho = 0.317192
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.067124
obj = -2.704839, rho = 0.310456
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.048204
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.033511
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.023297
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.016196
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.011259
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.007827
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*...*
optimization finished, #iter = 808
nu = 0.005441
obj = -2.720751, rho = 0.234376
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.604864
obj = -0.416768, rho = -0.154754
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.520000
obj = -0.507801, rho = -0.144108
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.441049
obj = -0.612909, rho = -0.206492
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.372691
obj = -0.734430, rho = -0.201058
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.309750
obj = -0.875891, rho = -0.209561
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.257559
obj = -1.048532, rho = -0.231639
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.213281
obj = -1.254757, rho = -0.285423
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.176831
obj = -1.496587, rho = -0.348556
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.147010
obj = -1.794932, rho = -0.362479
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.122341
obj = -2.158896, rho = -0.335485
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.104653
obj = -2.594481, rho = -0.219585
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.088635
obj = -3.009910, rho = -0.157834
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.074321
obj = -3.452772, rho = -0.261574
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
......*
optimization finished, #iter = 691
nu = 0.059905
obj = -3.822476, rho = -0.338450
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.045587
obj = -4.176412, rho = -0.358120
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.033720
obj = -4.554076, rho = -0.357664
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.028551
obj = -4.795818, rho = -0.195709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.019849
obj = -4.795818, rho = -0.195709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.013799
obj = -4.795818, rho = -0.195709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.009593
obj = -4.795818, rho = -0.195709
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.539591
obj = -0.376575, rho = -0.155757
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.470463
obj = -0.459800, rho = -0.150689
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.399170
obj = -0.557089, rho = -0.075844
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.331303
obj = -0.676750, rho = -0.101776
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.284284
obj = -0.823144, rho = -0.099762
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.239955
obj = -0.993170, rho = -0.083067
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.201766
obj = -1.193003, rho = 0.011263
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.166043
obj = -1.438249, rho = -0.030920
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.139328
obj = -1.756592, rho = -0.001288
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*......*
optimization finished, #iter = 671
nu = 0.118023
obj = -2.128328, rho = 0.004699
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.095396
obj = -2.629530, rho = 0.006499
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.081873
obj = -3.334922, rho = 0.066344
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.071662
obj = -4.273456, rho = 0.167077
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 220
nu = 0.067132
obj = -5.386813, rho = 0.174770
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.058977
obj = -6.589895, rho = 0.344001
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.052362
obj = -7.981332, rho = 0.772060
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.049741
obj = -8.971792, rho = 1.395373
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.037381
obj = -9.034027, rho = 1.589781
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.025987
obj = -9.034027, rho = 1.589781
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.018066
obj = -9.034027, rho = 1.589781
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.520306
obj = -0.338975, rho = -0.143289
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.432580
obj = -0.396156, rho = -0.113502
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.351663
obj = -0.460739, rho = -0.133815
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.287764
obj = -0.534402, rho = -0.091539
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.238284
obj = -0.614825, rho = -0.176953
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.185058
obj = -0.696800, rho = -0.212876
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.145878
obj = -0.798583, rho = -0.253639
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.115269
obj = -0.923423, rho = -0.265664
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.094954
obj = -1.076528, rho = -0.355825
nSV = 11, nBSV = 7
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.079477
obj = -1.207855, rho = -0.504954
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.062616
obj = -1.326600, rho = -0.575237
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.049899
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.034689
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.024116
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.016765
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.011655
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.008102
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.005633
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.003916
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.002722
obj = -1.361143, rho = -0.527264
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.595949
obj = -0.390569, rho = -0.121992
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.504515
obj = -0.460949, rho = -0.185091
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.410153
obj = -0.538988, rho = -0.198351
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.334758
obj = -0.627416, rho = -0.191470
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.270124
obj = -0.725935, rho = -0.182160
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.221169
obj = -0.838054, rho = -0.117988
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.178606
obj = -0.956579, rho = -0.094055
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.143153
obj = -1.078689, rho = -0.063856
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 282
nu = 0.113026
obj = -1.194805, rho = -0.132046
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*......*
optimization finished, #iter = 723
nu = 0.085889
obj = -1.309985, rho = -0.108238
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.066655
obj = -1.442961, rho = -0.054354
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.051652
obj = -1.568695, rho = 0.014013
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.039803
obj = -1.665644, rho = 0.010130
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.030034
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.020879
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.014515
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.010091
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.007015
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.004877
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.003390
obj = -1.695315, rho = -0.021643
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.583286
obj = -0.396656, rho = -0.304649
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.496491
obj = -0.480845, rho = -0.276432
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.432867
obj = -0.571676, rho = -0.203411
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.352327
obj = -0.670156, rho = -0.178966
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 206
nu = 0.287169
obj = -0.782516, rho = -0.150834
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.230902
obj = -0.913927, rho = -0.116066
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.190144
obj = -1.078126, rho = -0.197333
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.163665
obj = -1.243918, rho = -0.267165
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*...*
optimization finished, #iter = 393
nu = 0.131716
obj = -1.373800, rho = -0.376358
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
....*
optimization finished, #iter = 447
nu = 0.102463
obj = -1.488842, rho = -0.305868
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*
optimization finished, #iter = 351
nu = 0.074987
obj = -1.602167, rho = -0.298969
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.056468
obj = -1.728108, rho = -0.293576
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.045612
obj = -1.823594, rho = -0.425894
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.032439
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.022551
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.015677
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.010899
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.007577
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.005267
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.003662
obj = -1.830777, rho = -0.511060
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.550402
obj = -0.362224, rho = -0.083317
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.467307
obj = -0.425701, rho = -0.177799
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.384759
obj = -0.492013, rho = -0.191489
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.302773
obj = -0.566395, rho = -0.219164
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.237999
obj = -0.664172, rho = -0.209075
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.203334
obj = -0.784855, rho = -0.194724
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.164847
obj = -0.903401, rho = -0.228860
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.138636
obj = -1.023427, rho = -0.124915
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.107774
obj = -1.127688, rho = -0.167841
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*...*
optimization finished, #iter = 359
nu = 0.082445
obj = -1.220550, rho = -0.257791
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.060935
obj = -1.334971, rho = -0.297239
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.045624
obj = -1.486574, rho = -0.325121
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.036194
obj = -1.673044, rho = -0.344893
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.031119
obj = -1.801159, rho = -0.199458
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.022206
obj = -1.803007, rho = -0.176660
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.015437
obj = -1.803007, rho = -0.176660
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.010732
obj = -1.803007, rho = -0.176660
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.007461
obj = -1.803007, rho = -0.176660
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.005187
obj = -1.803007, rho = -0.176660
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.003606
obj = -1.803007, rho = -0.176660
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.563469
obj = -0.388796, rho = -0.144132
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.480000
obj = -0.474150, rho = -0.123740
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.405460
obj = -0.578195, rho = -0.114036
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.347745
obj = -0.704890, rho = -0.110657
nSV = 36, nBSV = 33
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.289710
obj = -0.857459, rho = -0.111344
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.245924
obj = -1.048964, rho = -0.081823
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.206780
obj = -1.290665, rho = -0.043512
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.175746
obj = -1.600031, rho = -0.004402
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.152795
obj = -1.986921, rho = 0.033637
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.136495
obj = -2.416463, rho = 0.070800
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.120680
obj = -2.873629, rho = 0.059767
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.102141
obj = -3.255020, rho = 0.057106
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
....*....*
optimization finished, #iter = 879
nu = 0.078099
obj = -3.592484, rho = 0.061296
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.059731
obj = -4.020925, rho = 0.083303
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.052097
obj = -4.381750, rho = 0.215909
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 447
nu = 0.037596
obj = -4.390477, rho = 0.217302
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 447
nu = 0.026137
obj = -4.390477, rho = 0.217302
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 447
nu = 0.018170
obj = -4.390477, rho = 0.217302
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 447
nu = 0.012632
obj = -4.390477, rho = 0.217302
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 447
nu = 0.008781
obj = -4.390477, rho = 0.217302
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.659074
obj = -0.472733, rho = -0.076296
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.576380
obj = -0.587119, rho = -0.129976
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.495800
obj = -0.728788, rho = -0.155129
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.429257
obj = -0.905485, rho = -0.153598
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.367775
obj = -1.128969, rho = -0.131475
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.310332
obj = -1.417247, rho = -0.100085
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.272598
obj = -1.795623, rho = -0.081830
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.237697
obj = -2.287106, rho = -0.115264
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.213979
obj = -2.916877, rho = -0.184251
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.190268
obj = -3.679830, rho = -0.044414
nSV = 25, nBSV = 14
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.166713
obj = -4.664092, rho = 0.004437
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 378
nu = 0.148010
obj = -5.909686, rho = 0.004233
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.129834
obj = -7.399103, rho = 0.007407
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*
optimization finished, #iter = 366
nu = 0.112346
obj = -9.349995, rho = -0.084042
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*........................*
optimization finished, #iter = 2622
nu = 0.098238
obj = -11.752340, rho = -0.139053
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 306
nu = 0.085678
obj = -14.969994, rho = -0.078337
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 271
nu = 0.073985
obj = -19.218135, rho = 0.080522
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*...*
optimization finished, #iter = 505
nu = 0.065370
obj = -25.009848, rho = 0.247600
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.....*.*
optimization finished, #iter = 639
nu = 0.063027
obj = -32.218741, rho = 0.918396
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.....*..*
optimization finished, #iter = 766
nu = 0.058214
obj = -40.046526, rho = 1.364454
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.630153
obj = -0.451386, rho = -0.435739
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.549062
obj = -0.560703, rho = -0.418792
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.476630
obj = -0.692944, rho = -0.436949
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.407264
obj = -0.858519, rho = -0.570359
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.346147
obj = -1.068035, rho = -0.574637
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.297728
obj = -1.331027, rho = -0.702633
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.258072
obj = -1.678509, rho = -0.706242
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.224715
obj = -2.118993, rho = -0.790475
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.202457
obj = -2.675313, rho = -0.983896
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.175383
obj = -3.348847, rho = -0.907751
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.155571
obj = -4.164348, rho = -0.882719
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.129710
obj = -5.193586, rho = -0.896870
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.111790
obj = -6.598376, rho = -0.955430
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.104505
obj = -8.285520, rho = -1.043584
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.095996
obj = -10.030760, rho = -1.474193
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*.*..*
optimization finished, #iter = 546
nu = 0.084622
obj = -11.359194, rho = -1.852728
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*.*
optimization finished, #iter = 314
nu = 0.067389
obj = -12.283184, rho = -1.772269
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*......*
optimization finished, #iter = 819
nu = 0.052109
obj = -12.592652, rho = -1.849643
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*......*
optimization finished, #iter = 819
nu = 0.036226
obj = -12.592652, rho = -1.849643
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*......*
optimization finished, #iter = 819
nu = 0.025184
obj = -12.592652, rho = -1.849643
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.547890
obj = -0.366856, rho = -0.273962
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.466324
obj = -0.436587, rho = -0.269460
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.396461
obj = -0.514366, rho = -0.361253
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.319494
obj = -0.595826, rho = -0.363999
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.258382
obj = -0.687808, rho = -0.314700
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.204463
obj = -0.795729, rho = -0.293326
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.168075
obj = -0.920187, rho = -0.303021
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.136210
obj = -1.054643, rho = -0.274268
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.109607
obj = -1.190352, rho = -0.252946
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.087639
obj = -1.307687, rho = -0.291781
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.069338
obj = -1.394993, rho = -0.516670
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.051826
obj = -1.429435, rho = -0.685518
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.036544
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.025405
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.017662
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.012278
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.008536
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.005934
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.004125
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.002868
obj = -1.434110, rho = -0.668444
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.560000
obj = -0.377394, rho = -0.081142
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.489368
obj = -0.446101, rho = -0.079583
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.411804
obj = -0.515978, rho = -0.124758
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.322553
obj = -0.585236, rho = -0.124690
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.255314
obj = -0.670230, rho = -0.138439
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.207795
obj = -0.760683, rho = -0.077648
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.158674
obj = -0.861816, rho = -0.087980
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.125451
obj = -0.989754, rho = -0.110874
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.100596
obj = -1.142596, rho = -0.149167
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.079769
obj = -1.320524, rho = -0.087243
nSV = 10, nBSV = 5
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.065499
obj = -1.522442, rho = 0.117094
nSV = 10, nBSV = 5
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.054356
obj = -1.712322, rho = 0.219758
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.045597
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.031698
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.022037
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.015320
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.010650
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.007404
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.005147
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.003578
obj = -1.788933, rho = 0.256464
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.599514
obj = -0.415183, rho = -0.018369
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.518563
obj = -0.503527, rho = 0.102474
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.440631
obj = -0.603819, rho = 0.151098
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.365611
obj = -0.717869, rho = 0.167402
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.301181
obj = -0.861823, rho = 0.114846
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.253503
obj = -1.034658, rho = 0.045584
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.217193
obj = -1.225998, rho = 0.071831
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.174753
obj = -1.437681, rho = 0.084040
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.142904
obj = -1.694116, rho = 0.053529
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.115396
obj = -2.008573, rho = 0.031222
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.094775
obj = -2.414565, rho = -0.025537
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.079070
obj = -2.919683, rho = -0.033581
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.066877
obj = -3.551100, rho = 0.145842
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.057375
obj = -4.243074, rho = 0.288535
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.047065
obj = -5.071109, rho = 0.376791
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.040033
obj = -6.035535, rho = 0.558478
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
....*..*
optimization finished, #iter = 662
nu = 0.034481
obj = -7.011311, rho = 0.787778
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
..*.............*
optimization finished, #iter = 1577
nu = 0.031038
obj = -7.500076, rho = 1.224080
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
..*.............*
optimization finished, #iter = 1577
nu = 0.021577
obj = -7.500076, rho = 1.224080
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
..*.............*
optimization finished, #iter = 1577
nu = 0.015000
obj = -7.500076, rho = 1.224080
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.559627
obj = -0.361499, rho = -0.250437
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.459087
obj = -0.426600, rho = -0.194604
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.376249
obj = -0.504739, rho = -0.200467
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.306378
obj = -0.597038, rho = -0.187865
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.247860
obj = -0.713457, rho = -0.188506
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.205612
obj = -0.867693, rho = -0.190746
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.171603
obj = -1.058389, rho = -0.111908
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.151396
obj = -1.286192, rho = -0.009396
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.125058
obj = -1.546920, rho = 0.022127
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.104391
obj = -1.874295, rho = 0.088009
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 202
nu = 0.085917
obj = -2.287921, rho = 0.090653
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.077603
obj = -2.780743, rho = -0.033486
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.070179
obj = -3.207275, rho = -0.146851
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*...*
optimization finished, #iter = 426
nu = 0.055325
obj = -3.442298, rho = -0.222428
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.041272
obj = -3.633436, rho = -0.267913
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.030141
obj = -3.866963, rho = -0.275237
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*..*
optimization finished, #iter = 318
nu = 0.024041
obj = -4.039039, rho = -0.362933
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*..*
optimization finished, #iter = 318
nu = 0.016713
obj = -4.039039, rho = -0.362933
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*..*
optimization finished, #iter = 318
nu = 0.011619
obj = -4.039039, rho = -0.362933
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*..*
optimization finished, #iter = 318
nu = 0.008077
obj = -4.039039, rho = -0.362933
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.581307
obj = -0.390447, rho = -0.300677
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.495093
obj = -0.466804, rho = -0.292863
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.412384
obj = -0.553864, rho = -0.363056
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.331736
obj = -0.659764, rho = -0.389303
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.273756
obj = -0.793099, rho = -0.457229
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.232014
obj = -0.961524, rho = -0.415715
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.195444
obj = -1.157851, rho = -0.344251
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.159802
obj = -1.398205, rho = -0.322979
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.138448
obj = -1.704478, rho = -0.185132
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.124071
obj = -2.003181, rho = -0.031560
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.100818
obj = -2.283902, rho = -0.088385
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.081582
obj = -2.525135, rho = -0.211558
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.062647
obj = -2.744343, rho = -0.218836
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.047404
obj = -2.949354, rho = -0.296094
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.037224
obj = -3.022102, rho = -0.542171
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.025878
obj = -3.022102, rho = -0.542171
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.017990
obj = -3.022102, rho = -0.542171
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.012507
obj = -3.022102, rho = -0.542171
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.008694
obj = -3.022102, rho = -0.542171
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.006044
obj = -3.022102, rho = -0.542171
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.569002
obj = -0.383180, rho = -0.092222
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.480655
obj = -0.456850, rho = -0.185808
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.399023
obj = -0.546105, rho = -0.147228
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.333405
obj = -0.648061, rho = -0.176239
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.274893
obj = -0.763735, rho = -0.124557
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.224627
obj = -0.905861, rho = -0.162474
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.188934
obj = -1.061681, rho = -0.280118
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.155529
obj = -1.230428, rho = -0.394744
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.127892
obj = -1.416349, rho = -0.330168
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.106713
obj = -1.559377, rho = -0.113657
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 283
nu = 0.082170
obj = -1.644973, rho = -0.159399
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.059538
obj = -1.704638, rho = -0.196283
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.043799
obj = -1.740108, rho = -0.205678
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.030852
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.021448
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.014910
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.010366
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.007206
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.005010
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.003483
obj = -1.741539, rho = -0.177160
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.609952
obj = -0.397324, rho = -0.328290
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.502609
obj = -0.468133, rho = -0.405792
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.409408
obj = -0.553954, rho = -0.447461
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.339534
obj = -0.656405, rho = -0.441492
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.282167
obj = -0.770625, rho = -0.430299
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.237404
obj = -0.893546, rho = -0.477533
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.185940
obj = -1.027484, rho = -0.525944
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.150389
obj = -1.185107, rho = -0.604639
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.120539
obj = -1.366650, rho = -0.661597
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.....*
optimization finished, #iter = 592
nu = 0.097197
obj = -1.561162, rho = -0.621391
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.080414
obj = -1.765831, rho = -0.764971
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*...........*
optimization finished, #iter = 1257
nu = 0.062904
obj = -1.932116, rho = -0.907281
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.046518
obj = -2.120531, rho = -0.907489
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.036434
obj = -2.335559, rho = -0.883106
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.029851
obj = -2.423865, rho = -0.647068
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.020752
obj = -2.423865, rho = -0.647068
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.014427
obj = -2.423865, rho = -0.647068
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.010029
obj = -2.423865, rho = -0.647068
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.006972
obj = -2.423865, rho = -0.647068
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.004847
obj = -2.423865, rho = -0.647068
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
No description has been provided for this image
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt


def data(N,sigma):   
    w = np.ones(10)/np.sqrt(10)   
    w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)   
    w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)   
    x = np.zeros((4,10))   
    x[1,:] = x[0,:] + sigma*w1   
    x[2,:] = x[0,:] + sigma*w2   
    x[3,:] = x[2,:] + sigma*w1   
    X1 = x + sigma*matlib.repmat(w,4,1)/2   
    X2 = x - sigma*matlib.repmat(w,4,1)/2   
    X1 = matlib.repmat(X1,2*N,1)   
    X2 = matlib.repmat(X2,2*N,1)   
    X = np.concatenate((X1, X2), axis=0)   
    Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)   
    Z = np.random.permutation(16*N)   
    Z = Z[:N]   
    X = X[Z,:]   
    X = X + 0.2*sigma*np.random.randn(N,10)   
    Y = Y[Z]

    return X, Y

# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-1, 1, 20)  # Logarithmically spaced values between 0.01 and 10

# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 0.5

# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):

    # Generate data
    X_train, y_train = data(100,sigma)
    X_test, y_test = data(1000, sigma)

    for lam in lambda_values:
        
        # Train SVM
        svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
        param = svm_parameter(f'-t 0 -c {lam}')
        model = svm_train(svm_problem_setup, param)
        
        # Predict on training and test data
        i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
        i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
        
        # Calculate errors
        training_errors.append(100 - train_accuracy[0])  # Convert to error percentage
        test_errors.append(100 - test_accuracy[0])  # Convert to error percentage

# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)

avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)

lambda_values_log = np.log10(lambda_values)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')

plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.1,10)@ σ = 0.5')
plt.legend()
plt.show()
*
optimization finished, #iter = 47
nu = 0.920653
obj = -6.893917, rho = -0.208724
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.071775, rho = -0.196091
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.807649
obj = -9.382671, rho = -0.181255
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.747182
obj = -10.815554, rho = -0.208027
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.673562
obj = -12.404873, rho = -0.127842
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.608527
obj = -14.238098, rho = -0.142273
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.548629
obj = -16.340639, rho = -0.132620
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.493627
obj = -18.724916, rho = -0.144774
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.447154
obj = -21.469439, rho = -0.134863
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.400000
obj = -24.547398, rho = -0.171606
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.356683
obj = -28.104154, rho = -0.142633
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.325209
obj = -32.102540, rho = -0.099993
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.287196
obj = -36.676279, rho = -0.083277
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.260024
obj = -41.911937, rho = 0.013156
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.228853
obj = -48.054697, rho = 0.045051
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.208441
obj = -55.436593, rho = -0.083898
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.187521
obj = -63.704364, rho = -0.208576
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.168414
obj = -73.310801, rho = -0.234706
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 283
nu = 0.150719
obj = -84.589459, rho = -0.284665
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.133560
obj = -98.286541, rho = -0.310343
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -7.066350, rho = 0.102575
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 91% (91/100) (classification)
Accuracy = 87.7% (877/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.868137
obj = -8.363676, rho = 0.015350
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 94% (94/100) (classification)
Accuracy = 92.7% (927/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.822684
obj = -9.807341, rho = -0.033492
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 95% (95/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760000
obj = -11.420537, rho = -0.073858
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.713076
obj = -13.221266, rho = -0.135900
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.648728
obj = -15.190445, rho = -0.143158
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.585490
obj = -17.395809, rho = -0.122583
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.523559
obj = -19.895625, rho = -0.099341
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.475911
obj = -22.795985, rho = -0.048232
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.419254
obj = -26.156579, rho = -0.064359
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.377304
obj = -30.148359, rho = -0.048596
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.335623
obj = -34.860943, rho = -0.013997
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.308547
obj = -40.546699, rho = 0.037652
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.280686
obj = -46.854192, rho = 0.118420
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.252479
obj = -54.360316, rho = 0.128523
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.232677
obj = -62.921242, rho = 0.096106
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.212995
obj = -72.587846, rho = 0.116513
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*...*
optimization finished, #iter = 317
nu = 0.195776
obj = -83.098242, rho = 0.261299
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.171555
obj = -95.370595, rho = 0.289806
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.152905
obj = -110.204880, rho = 0.311009
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.889517
obj = -6.513745, rho = -0.402217
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.848427
obj = -7.545749, rho = -0.397463
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780000
obj = -8.631912, rho = -0.407866
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.709475
obj = -9.791737, rho = -0.398456
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.632508
obj = -11.024532, rho = -0.373340
nSV = 67, nBSV = 59
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.558520
obj = -12.414179, rho = -0.371263
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.499911
obj = -13.916362, rho = -0.372814
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.431679
obj = -15.606091, rho = -0.370773
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.384723
obj = -17.564964, rho = -0.347898
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.340178
obj = -19.605314, rho = -0.305319
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.298086
obj = -21.927016, rho = -0.307311
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.265013
obj = -24.518114, rho = -0.303501
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.231489
obj = -27.261610, rho = -0.340398
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.202509
obj = -30.370943, rho = -0.329519
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.173149
obj = -33.962868, rho = -0.343735
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.150926
obj = -38.310406, rho = -0.425641
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.133980
obj = -43.260203, rho = -0.461205
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.121562
obj = -48.749443, rho = -0.586887
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.110591
obj = -53.920297, rho = -0.781707
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.096351
obj = -59.173110, rho = -0.838287
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.821038, rho = 0.050454
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 90.1% (901/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.878903
obj = -7.942320, rho = -0.082082
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.809991
obj = -9.128631, rho = -0.076787
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.748711
obj = -10.390967, rho = -0.133950
nSV = 77, nBSV = 72
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.664525
obj = -11.753432, rho = -0.134739
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.595670
obj = -13.260754, rho = -0.160467
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.537561
obj = -14.856341, rho = -0.176270
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.466329
obj = -16.611224, rho = -0.170209
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.412492
obj = -18.622856, rho = -0.190284
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.363999
obj = -20.856477, rho = -0.197021
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.317889
obj = -23.257496, rho = -0.253301
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.281390
obj = -25.926561, rho = -0.309784
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.247480
obj = -28.769433, rho = -0.294715
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.212318
obj = -31.871890, rho = -0.282528
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.189151
obj = -35.400873, rho = -0.318843
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.164850
obj = -38.905321, rho = -0.301042
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.142131
obj = -42.761150, rho = -0.244222
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.122043
obj = -47.180797, rho = -0.201624
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.112837
obj = -51.204829, rho = -0.099564
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.099118
obj = -53.911988, rho = -0.098585
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.905655
obj = -6.818175, rho = -0.366895
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 94% (94/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -7.966612, rho = -0.292651
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.816006
obj = -9.192645, rho = -0.207712
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.748152
obj = -10.471687, rho = -0.196532
nSV = 77, nBSV = 72
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.675033
obj = -11.852611, rho = -0.245006
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.598908
obj = -13.367450, rho = -0.221157
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.533510
obj = -15.105113, rho = -0.203459
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.472737
obj = -16.977589, rho = -0.157891
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.422582
obj = -19.001747, rho = -0.158936
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.364515
obj = -21.269134, rho = -0.124857
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.315794
obj = -23.988896, rho = -0.126065
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.282149
obj = -27.124629, rho = -0.097330
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.267982
obj = -30.292300, rho = -0.030751
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.232696
obj = -33.014584, rho = 0.042828
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.196660
obj = -36.063206, rho = 0.066928
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.170260
obj = -39.496531, rho = 0.027763
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.145246
obj = -43.154943, rho = 0.016199
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.123941
obj = -47.283634, rho = 0.067498
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.108174
obj = -51.766607, rho = 0.092589
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.092132
obj = -56.491967, rho = 0.119752
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -6.850651, rho = -0.537696
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 86% (86/100) (classification)
Accuracy = 82.2% (822/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.118212, rho = -0.410897
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.820000
obj = -9.435902, rho = -0.374152
nSV = 82, nBSV = 81
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.756636
obj = -10.855947, rho = -0.321991
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.689733
obj = -12.387917, rho = -0.299866
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.620000
obj = -14.108316, rho = -0.259028
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.556406
obj = -16.005896, rho = -0.204557
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.496418
obj = -18.117264, rho = -0.222341
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.445469
obj = -20.443099, rho = -0.262809
nSV = 49, nBSV = 40
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.400000
obj = -22.971875, rho = -0.281301
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.354535
obj = -25.594983, rho = -0.266614
nSV = 36, nBSV = 33
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.319112
obj = -28.267312, rho = -0.259807
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.275898
obj = -30.965221, rho = -0.248898
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.235814
obj = -33.890551, rho = -0.267845
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.201566
obj = -37.198785, rho = -0.256908
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.179695
obj = -40.431141, rho = -0.314611
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.154383
obj = -43.420742, rho = -0.417940
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*....*
optimization finished, #iter = 407
nu = 0.129255
obj = -46.586064, rho = -0.430341
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*
optimization finished, #iter = 255
nu = 0.107712
obj = -50.143141, rho = -0.495412
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.091073
obj = -54.110014, rho = -0.587226
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.613752, rho = 0.088904
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.841987
obj = -7.689214, rho = -0.044152
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.784132
obj = -8.862002, rho = -0.017108
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.709798
obj = -10.130637, rho = -0.051673
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.633119
obj = -11.580416, rho = -0.071414
nSV = 68, nBSV = 61
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.576196
obj = -13.239456, rho = -0.008454
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.517437
obj = -15.054211, rho = -0.014202
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.462098
obj = -17.107722, rho = -0.024147
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.412705
obj = -19.378798, rho = -0.010805
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.361765
obj = -22.074501, rho = 0.000441
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.323077
obj = -25.277325, rho = -0.068191
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.289126
obj = -28.959974, rho = -0.022057
nSV = 31, nBSV = 28
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.260680
obj = -33.118036, rho = -0.033055
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.226949
obj = -38.148970, rho = -0.040232
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.201953
obj = -44.420419, rho = -0.075504
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.183440
obj = -52.100467, rho = -0.116312
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.167463
obj = -61.322034, rho = -0.135273
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 95% (95/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.152330
obj = -72.556953, rho = -0.133783
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.142384
obj = -86.077658, rho = -0.137609
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.136117
obj = -101.575793, rho = -0.151506
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.800184, rho = -0.322408
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.865303
obj = -7.947035, rho = -0.252260
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.801783
obj = -9.192233, rho = -0.190533
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.733301
obj = -10.563201, rho = -0.160039
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.663252
obj = -12.088045, rho = -0.171110
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.594089
obj = -13.820207, rho = -0.179478
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.540228
obj = -15.799684, rho = -0.243183
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.499350
obj = -17.864525, rho = -0.201742
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.447872
obj = -20.012444, rho = -0.281943
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.387077
obj = -22.354324, rho = -0.274253
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.347216
obj = -24.875617, rho = -0.236266
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.308131
obj = -27.493605, rho = -0.251688
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.262415
obj = -30.256062, rho = -0.288259
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.231809
obj = -33.354168, rho = -0.276286
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.201466
obj = -36.308609, rho = -0.236027
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 200
nu = 0.169565
obj = -39.672076, rho = -0.249507
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.143731
obj = -43.511615, rho = -0.253793
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.127831
obj = -47.555142, rho = -0.185251
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.111016
obj = -51.689306, rho = -0.205947
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.098002
obj = -54.908945, rho = -0.231898
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.169704, rho = -0.530187
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 90% (90/100) (classification)
Accuracy = 87.5% (875/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.883777
obj = -8.457447, rho = -0.456166
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.835033
obj = -9.875820, rho = -0.401109
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -11.437388, rho = -0.330369
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.720000
obj = -13.162606, rho = -0.318358
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.655594
obj = -14.979886, rho = -0.254566
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.586977
obj = -17.038102, rho = -0.264381
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.524347
obj = -19.310782, rho = -0.321722
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.477774
obj = -21.734238, rho = -0.281662
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.423488
obj = -24.382134, rho = -0.239877
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.374916
obj = -27.169728, rho = -0.288483
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.325868
obj = -30.310499, rho = -0.265375
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.287421
obj = -33.864692, rho = -0.237342
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.255916
obj = -37.631601, rho = -0.252005
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.222326
obj = -41.472512, rho = -0.186471
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.192841
obj = -45.817263, rho = -0.168513
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.173041
obj = -50.105262, rho = -0.126725
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.150107
obj = -53.995943, rho = -0.091213
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.125246
obj = -57.896423, rho = -0.061456
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.107271
obj = -62.101157, rho = -0.051312
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.956129
obj = -7.361636, rho = -0.009889
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -8.674427, rho = -0.060955
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.858942
obj = -10.131080, rho = -0.106749
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -11.736026, rho = -0.103008
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.727295
obj = -13.488804, rho = -0.152975
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.671183
obj = -15.395603, rho = -0.124631
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.601083
obj = -17.544395, rho = -0.143711
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.539801
obj = -19.941992, rho = -0.121169
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.482445
obj = -22.613222, rho = -0.104654
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.427673
obj = -25.624454, rho = -0.077446
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.381384
obj = -28.969601, rho = -0.090067
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.332897
obj = -32.895676, rho = -0.097142
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.300000
obj = -37.440487, rho = -0.113820
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.270392
obj = -42.300942, rho = -0.150723
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.244667
obj = -47.730215, rho = -0.139853
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*...*
optimization finished, #iter = 312
nu = 0.218067
obj = -53.235155, rho = -0.199710
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.187246
obj = -59.582045, rho = -0.233289
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.164932
obj = -66.976735, rho = -0.242581
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 183
nu = 0.145030
obj = -75.417888, rho = -0.282983
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.125988
obj = -85.356676, rho = -0.288977
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -6.706609, rho = -0.141975
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 96% (96/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.841844
obj = -7.829029, rho = -0.181233
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.785675
obj = -9.057406, rho = -0.295067
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.728985
obj = -10.379018, rho = -0.259648
nSV = 76, nBSV = 71
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.660789
obj = -11.837113, rho = -0.218675
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.591113
obj = -13.471700, rho = -0.209194
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.526297
obj = -15.319887, rho = -0.207530
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.468612
obj = -17.428117, rho = -0.214012
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.423325
obj = -19.800150, rho = -0.192258
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.385839
obj = -22.392236, rho = -0.124968
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.337943
obj = -25.148123, rho = -0.091006
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.300664
obj = -28.227763, rho = -0.111175
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.264180
obj = -31.565322, rho = -0.118061
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.228445
obj = -35.479384, rho = -0.110696
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.204259
obj = -40.012306, rho = -0.080788
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.181711
obj = -44.801812, rho = -0.076601
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.160291
obj = -50.014660, rho = -0.061987
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.146962
obj = -55.154542, rho = 0.072471
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.131050
obj = -59.511519, rho = 0.205405
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.112363
obj = -63.292564, rho = 0.239255
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -6.267033, rho = -0.348940
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.795578
obj = -7.303017, rho = -0.279997
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.729484
obj = -8.462397, rho = -0.306798
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.680000
obj = -9.717842, rho = -0.257419
nSV = 69, nBSV = 67
Total nSV = 69
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.614519
obj = -11.093163, rho = -0.219663
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.560000
obj = -12.586559, rho = -0.232503
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.495803
obj = -14.239024, rho = -0.239624
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.432719
obj = -16.168995, rho = -0.238217
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.392766
obj = -18.362319, rho = -0.214273
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.350069
obj = -20.760470, rho = -0.253950
nSV = 37, nBSV = 34
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.311001
obj = -23.401532, rho = -0.236268
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.271330
obj = -26.523609, rho = -0.230478
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.237636
obj = -30.270453, rho = -0.220192
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.213408
obj = -34.820258, rho = -0.249273
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.197504
obj = -39.694490, rho = -0.298657
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.185509
obj = -44.627183, rho = -0.176068
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.166860
obj = -48.669121, rho = -0.040352
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.142876
obj = -52.849731, rho = -0.044385
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*..*
optimization finished, #iter = 329
nu = 0.119441
obj = -57.591972, rho = -0.009754
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.101070
obj = -63.135417, rho = -0.028033
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.934505
obj = -6.901413, rho = -0.272236
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.855531
obj = -8.072136, rho = -0.252124
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.804351
obj = -9.411501, rho = -0.222025
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.746371
obj = -10.847244, rho = -0.161704
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.690215
obj = -12.394337, rho = -0.124911
nSV = 70, nBSV = 68
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.619933
obj = -14.040849, rho = -0.119125
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.557150
obj = -15.882343, rho = -0.106328
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.496081
obj = -17.861083, rho = -0.013058
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.438756
obj = -20.064735, rho = -0.030895
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.397736
obj = -22.357728, rho = 0.003604
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.344617
obj = -24.800505, rho = -0.021149
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.298724
obj = -27.521155, rho = -0.058731
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.259752
obj = -30.553057, rho = -0.055799
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.225339
obj = -34.047229, rho = -0.044329
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.197709
obj = -37.980733, rho = -0.055133
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.171695
obj = -42.454012, rho = -0.062179
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.152167
obj = -47.471327, rho = -0.015757
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.130737
obj = -53.105819, rho = 0.005149
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.116456
obj = -59.620266, rho = 0.143063
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.104669
obj = -66.144509, rho = 0.177936
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.903727
obj = -6.706177, rho = -0.231181
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.855560
obj = -7.815368, rho = -0.231158
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.781994
obj = -9.025535, rho = -0.154632
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.722284
obj = -10.355825, rho = -0.134012
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.652032
obj = -11.808425, rho = -0.121004
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.593890
obj = -13.404818, rho = -0.080977
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.541367
obj = -15.090674, rho = -0.120854
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.473491
obj = -16.861202, rho = -0.128525
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.415301
obj = -18.871264, rho = -0.115890
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.366267
obj = -21.143271, rho = -0.074451
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.318945
obj = -23.714606, rho = -0.049406
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.282396
obj = -26.694462, rho = -0.028675
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.251746
obj = -29.749273, rho = -0.020768
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.217357
obj = -33.338653, rho = -0.021211
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.**.*
optimization finished, #iter = 151
nu = 0.193407
obj = -37.335851, rho = 0.013794
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.168442
obj = -41.862872, rho = -0.009963
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.148052
obj = -46.962426, rho = -0.058349
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*..*
optimization finished, #iter = 253
nu = 0.132079
obj = -52.361706, rho = -0.100423
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.117425
obj = -58.332821, rho = -0.122363
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.102847
obj = -64.200554, rho = -0.186695
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -6.372040, rho = -0.574342
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 88% (88/100) (classification)
Accuracy = 83.6% (836/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.807795
obj = -7.439749, rho = -0.532563
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 89.3% (893/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760000
obj = -8.586858, rho = -0.460530
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.691287
obj = -9.793425, rho = -0.437999
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.616750
obj = -11.143498, rho = -0.423403
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.560000
obj = -12.673859, rho = -0.406856
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.503319
obj = -14.311254, rho = -0.350149
nSV = 52, nBSV = 50
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.447644
obj = -16.040360, rho = -0.336746
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.393225
obj = -18.057190, rho = -0.321073
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.347099
obj = -20.325685, rho = -0.349622
nSV = 37, nBSV = 34
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.316527
obj = -22.679405, rho = -0.354146
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.278895
obj = -25.155013, rho = -0.352974
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.240544
obj = -27.768186, rho = -0.333357
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.210775
obj = -30.654904, rho = -0.492211
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.183967
obj = -33.618903, rho = -0.606732
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.165218
obj = -36.621969, rho = -0.657469
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.141372
obj = -39.120953, rho = -0.681751
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.119305
obj = -41.399057, rho = -0.693992
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.102116
obj = -43.344478, rho = -0.600057
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.**...........*
optimization finished, #iter = 1220
nu = 0.084281
obj = -44.805699, rho = -0.584526
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.845388, rho = 0.140807
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 92% (92/100) (classification)
Accuracy = 87.1% (871/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -7.994982, rho = 0.008031
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.812171
obj = -9.203529, rho = -0.003833
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.748121
obj = -10.519965, rho = -0.025066
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.666109
obj = -11.937279, rho = -0.041817
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.601130
obj = -13.518021, rho = -0.116662
nSV = 63, nBSV = 56
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.536490
obj = -15.289767, rho = -0.100465
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.473473
obj = -17.250034, rho = -0.055348
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.419170
obj = -19.476742, rho = -0.086583
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.374935
obj = -21.940373, rho = -0.125277
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.330643
obj = -24.701885, rho = -0.081167
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.294560
obj = -27.770100, rho = -0.098485
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.261106
obj = -31.026960, rho = -0.067097
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.229051
obj = -34.692845, rho = -0.036974
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.205584
obj = -38.715327, rho = -0.067280
nSV = 23, nBSV = 19
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.180111
obj = -42.643474, rho = -0.086153
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.161344
obj = -46.783817, rho = -0.041319
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.142250
obj = -50.258734, rho = -0.081823
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.119018
obj = -53.375832, rho = -0.108784
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.100840
obj = -56.507358, rho = -0.077231
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.693814, rho = 0.062619
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.857748
obj = -7.780495, rho = -0.058028
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.793083
obj = -8.953549, rho = -0.063910
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.718829
obj = -10.220923, rho = -0.073595
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.657498
obj = -11.659714, rho = -0.051792
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.586437
obj = -13.215381, rho = -0.083556
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.523668
obj = -14.923341, rho = -0.053208
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.467724
obj = -16.769157, rho = -0.127234
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.411745
obj = -18.853807, rho = -0.176533
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.365312
obj = -21.169610, rho = -0.117304
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.325374
obj = -23.720701, rho = -0.039515
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.284330
obj = -26.423298, rho = -0.020361
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.244324
obj = -29.566397, rho = -0.060819
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.215690
obj = -33.244343, rho = -0.024295
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.196622
obj = -37.068348, rho = 0.035506
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.169583
obj = -41.195860, rho = 0.080183
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.148154
obj = -45.902422, rho = 0.096889
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.130531
obj = -50.733332, rho = 0.061408
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.110902
obj = -56.393660, rho = 0.036160
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.095694
obj = -63.221736, rho = 0.049930
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.918339
obj = -6.881465, rho = -0.289892
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.862882
obj = -8.056760, rho = -0.306107
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.804350
obj = -9.371627, rho = -0.265760
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.736034
obj = -10.845000, rho = -0.248807
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.666590
obj = -12.510968, rho = -0.228635
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.625120
obj = -14.377422, rho = -0.115528
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.560186
obj = -16.389907, rho = -0.130715
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.502474
obj = -18.628693, rho = -0.111406
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.455219
obj = -21.131113, rho = -0.091225
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.398770
obj = -23.889896, rho = -0.027578
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.355396
obj = -27.106373, rho = -0.073039
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.326256
obj = -30.541151, rho = 0.002530
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.291263
obj = -34.035896, rho = -0.044315
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.250726
obj = -37.838676, rho = -0.035072
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.214547
obj = -42.489956, rho = -0.031071
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.192278
obj = -47.870463, rho = -0.087811
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.168261
obj = -53.897851, rho = -0.074860
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.148827
obj = -60.692394, rho = -0.087576
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.130474
obj = -68.175579, rho = -0.142630
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.114500
obj = -77.252346, rho = -0.196946
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.872502
obj = -6.712718, rho = -0.007363
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 92% (92/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -7.931893, rho = -0.090101
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.785548
obj = -9.249763, rho = -0.050992
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.732771
obj = -10.713856, rho = -0.014262
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 95% (95/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.680190
obj = -12.289230, rho = -0.073314
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.603594
obj = -14.017817, rho = -0.106603
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.546862
obj = -15.980842, rho = -0.145965
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.487073
obj = -18.192799, rho = -0.161197
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.434834
obj = -20.742898, rho = -0.218802
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.382365
obj = -23.751092, rho = -0.215575
nSV = 40, nBSV = 37
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.358181
obj = -27.105248, rho = -0.242337
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.313093
obj = -30.722189, rho = -0.276310
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.278859
obj = -34.959424, rho = -0.264905
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.251514
obj = -39.722249, rho = -0.175232
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.222069
obj = -45.172917, rho = -0.224015
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.197391
obj = -51.653013, rho = -0.289386
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.178801
obj = -58.776297, rho = -0.352893
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.161596
obj = -66.756813, rho = -0.417758
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.140082
obj = -75.783490, rho = -0.411607
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.123481
obj = -86.980115, rho = -0.385193
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.942067
obj = -7.345989, rho = -0.268589
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -8.654887, rho = -0.176437
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.859694
obj = -10.051220, rho = -0.167753
nSV = 89, nBSV = 84
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.789987
obj = -11.644881, rho = -0.119770
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.729239
obj = -13.364361, rho = -0.094112
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.656999
obj = -15.338451, rho = -0.145634
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.603467
obj = -17.493664, rho = -0.105052
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.539987
obj = -19.864353, rho = -0.146245
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.480732
obj = -22.520372, rho = -0.137207
nSV = 52, nBSV = 43
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.422058
obj = -25.583619, rho = -0.122666
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.371935
obj = -29.170052, rho = -0.154173
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.335070
obj = -33.402110, rho = -0.222930
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.308464
obj = -38.013302, rho = -0.169003
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.272152
obj = -43.110127, rho = -0.147417
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.239412
obj = -49.082669, rho = -0.128202
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.213247
obj = -56.041944, rho = -0.130964
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.187941
obj = -64.503271, rho = -0.139952
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.171797
obj = -74.343596, rho = -0.170954
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.158163
obj = -84.772534, rho = -0.282793
nSV = 23, nBSV = 12
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.139032
obj = -97.057972, rho = -0.378863
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -7.036974, rho = -0.237486
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -8.273207, rho = -0.208120
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.817701
obj = -9.653808, rho = -0.210890
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.755288
obj = -11.194396, rho = -0.262625
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.688059
obj = -12.957745, rho = -0.275595
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.629425
obj = -14.971063, rho = -0.261676
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.573252
obj = -17.177220, rho = -0.220664
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.520239
obj = -19.668581, rho = -0.185864
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.468473
obj = -22.509535, rho = -0.161905
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.422550
obj = -25.702142, rho = -0.084062
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.380000
obj = -29.302774, rho = -0.085224
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.347857
obj = -33.110249, rho = -0.213741
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.306376
obj = -37.246508, rho = -0.262856
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.265925
obj = -42.105353, rho = -0.293150
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.233081
obj = -47.941173, rho = -0.253109
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.215333
obj = -54.688990, rho = -0.195668
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.189671
obj = -61.824756, rho = -0.180218
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.167161
obj = -70.263257, rho = -0.165365
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.148186
obj = -80.076094, rho = -0.114725
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.133693
obj = -91.203725, rho = -0.028743
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -7.095504, rho = 0.196322
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 93% (93/100) (classification)
Accuracy = 88.6% (886/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.904489
obj = -8.312298, rho = 0.031546
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.855422
obj = -9.574524, rho = 0.003908
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.775833
obj = -10.918115, rho = -0.035340
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.698204
obj = -12.374084, rho = -0.088575
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.620879
obj = -14.015187, rho = -0.117682
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.554218
obj = -15.814890, rho = -0.063750
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.497874
obj = -17.806603, rho = -0.079894
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.443683
obj = -19.873153, rho = -0.108947
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.385461
obj = -22.142835, rho = -0.102278
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.332744
obj = -24.825077, rho = -0.110263
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.294527
obj = -27.949145, rho = -0.062291
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.256394
obj = -31.480211, rho = -0.028137
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.226691
obj = -35.692868, rho = -0.041976
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.197538
obj = -40.572577, rho = -0.109950
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.173774
obj = -46.616341, rho = -0.094281
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.153871
obj = -53.973294, rho = -0.062824
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.139882
obj = -62.793829, rho = -0.108018
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.130468
obj = -72.901916, rho = -0.209453
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.119278
obj = -84.085370, rho = -0.239102
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.891798
obj = -6.490916, rho = -0.245196
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.839349
obj = -7.529185, rho = -0.252059
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.765115
obj = -8.652350, rho = -0.177381
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.700000
obj = -9.880200, rho = -0.132564
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.622842
obj = -11.243164, rho = -0.115687
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.559244
obj = -12.793394, rho = -0.125947
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.500000
obj = -14.581194, rho = -0.138424
nSV = 51, nBSV = 48
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.447269
obj = -16.553406, rho = -0.190714
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.397463
obj = -18.815547, rho = -0.259896
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.366294
obj = -21.273000, rho = -0.229385
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.327990
obj = -23.821173, rho = -0.272159
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.289488
obj = -26.543795, rho = -0.224936
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.257093
obj = -29.372698, rho = -0.230650
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.220376
obj = -32.398948, rho = -0.231820
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.189792
obj = -35.760787, rho = -0.272772
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.165935
obj = -39.652155, rho = -0.342088
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.144601
obj = -43.747266, rho = -0.417219
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.127151
obj = -47.998716, rho = -0.521445
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.106976
obj = -52.693604, rho = -0.542702
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.093614
obj = -58.109198, rho = -0.657051
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.895542
obj = -6.622383, rho = -0.322907
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.834163
obj = -7.731372, rho = -0.289153
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780126
obj = -8.968436, rho = -0.252015
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.718934
obj = -10.288271, rho = -0.239998
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.660000
obj = -11.716668, rho = -0.186336
nSV = 67, nBSV = 65
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.584832
obj = -13.255191, rho = -0.160132
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.519906
obj = -15.006228, rho = -0.141731
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.456298
obj = -17.064200, rho = -0.178007
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.423394
obj = -19.303006, rho = -0.145293
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.366067
obj = -21.754319, rho = -0.144378
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.332130
obj = -24.594804, rho = -0.121345
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.291006
obj = -27.544134, rho = -0.117471
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.253297
obj = -31.063824, rho = -0.099773
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.222795
obj = -35.130854, rho = -0.102715
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.199214
obj = -39.766879, rho = -0.028986
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.178019
obj = -44.929376, rho = -0.058607
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.161096
obj = -50.509348, rho = -0.216550
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.146659
obj = -55.943664, rho = -0.329014
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.126528
obj = -61.522448, rho = -0.396282
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.110573
obj = -67.391101, rho = -0.491075
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.922565
obj = -7.209074, rho = -0.253096
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 88% (88/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.888687
obj = -8.511366, rho = -0.165522
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 92% (92/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.832945
obj = -9.979461, rho = -0.177607
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 93% (93/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.766181
obj = -11.680855, rho = -0.146991
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 93% (93/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.710631
obj = -13.623589, rho = -0.102154
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 94% (94/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.658108
obj = -15.806999, rho = -0.093675
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 93% (93/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.597651
obj = -18.271658, rho = -0.082350
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 94% (94/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.540000
obj = -21.137456, rho = -0.111784
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 96% (96/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.495294
obj = -24.458722, rho = -0.067218
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.442672
obj = -28.257462, rho = -0.060530
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.397465
obj = -32.909509, rho = -0.055166
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.364185
obj = -38.441273, rho = -0.043832
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.336805
obj = -44.701964, rho = 0.011029
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.302875
obj = -52.014818, rho = 0.017903
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.274356
obj = -60.806835, rho = -0.011405
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.249882
obj = -71.410994, rho = -0.065503
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.236545
obj = -83.815574, rho = 0.003773
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.215362
obj = -98.043678, rho = -0.005814
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.198107
obj = -114.569148, rho = -0.020449
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.185486
obj = -133.459575, rho = -0.070219
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -7.021877, rho = -0.316928
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.886820
obj = -8.186186, rho = -0.206820
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.845951
obj = -9.422451, rho = -0.217212
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.769298
obj = -10.711724, rho = -0.213957
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.696591
obj = -12.067181, rho = -0.199195
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.605045
obj = -13.584349, rho = -0.188946
nSV = 63, nBSV = 56
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.533912
obj = -15.386547, rho = -0.183888
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.477858
obj = -17.411594, rho = -0.141576
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.426428
obj = -19.571553, rho = -0.093455
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.379123
obj = -22.017831, rho = -0.170588
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.334140
obj = -24.775491, rho = -0.151775
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.293146
obj = -27.763925, rho = -0.163985
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 180
nu = 0.264731
obj = -31.069043, rho = -0.120260
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.232972
obj = -34.435963, rho = -0.078975
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*...*
optimization finished, #iter = 365
nu = 0.199609
obj = -38.146822, rho = -0.087039
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.173666
obj = -42.555588, rho = -0.067269
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.159923
obj = -47.249198, rho = -0.157838
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.142065
obj = -51.267377, rho = -0.146269
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.....*
optimization finished, #iter = 771
nu = 0.123599
obj = -54.488238, rho = -0.146761
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*..*
optimization finished, #iter = 375
nu = 0.103841
obj = -57.201530, rho = -0.127794
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.054967, rho = -0.424395
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 92% (92/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.876280
obj = -8.302720, rho = -0.354388
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.836484
obj = -9.663598, rho = -0.273800
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -11.090114, rho = -0.193677
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.706106
obj = -12.600884, rho = -0.152549
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.632647
obj = -14.282973, rho = -0.141007
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.562976
obj = -16.178823, rho = -0.121599
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.497321
obj = -18.307788, rho = -0.152285
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.440000
obj = -20.831900, rho = -0.153388
nSV = 45, nBSV = 43
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.401327
obj = -23.576061, rho = -0.169805
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.359948
obj = -26.425535, rho = -0.136107
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.316768
obj = -29.578627, rho = -0.179819
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.275998
obj = -33.070603, rho = -0.115453
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.243147
obj = -36.980768, rho = -0.057664
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.210281
obj = -41.580659, rho = -0.035523
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.187667
obj = -46.906019, rho = -0.049350
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.165503
obj = -52.656306, rho = -0.061750
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.144263
obj = -59.356086, rho = -0.098749
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.131371
obj = -66.679131, rho = -0.100207
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.111763
obj = -74.920973, rho = -0.084355
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.909222
obj = -6.749875, rho = -0.284603
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.849806
obj = -7.878065, rho = -0.289931
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.793204
obj = -9.115695, rho = -0.282555
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.735315
obj = -10.468819, rho = -0.245514
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.672784
obj = -11.912939, rho = -0.228843
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.594430
obj = -13.492595, rho = -0.225188
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.529731
obj = -15.319985, rho = -0.233056
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.483808
obj = -17.287902, rho = -0.233853
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.416109
obj = -19.484346, rho = -0.234105
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.380000
obj = -22.008581, rho = -0.192340
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.347733
obj = -24.341689, rho = -0.124634
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.297041
obj = -26.865290, rho = -0.100165
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.256235
obj = -29.666989, rho = -0.028888
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.221361
obj = -32.853733, rho = -0.007031
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.197195
obj = -36.249103, rho = 0.061936
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.171809
obj = -39.650300, rho = 0.131003
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.149617
obj = -43.100220, rho = 0.124051
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.129323
obj = -46.395951, rho = 0.121944
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*..*
optimization finished, #iter = 476
nu = 0.108144
obj = -49.679065, rho = 0.128962
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.092024
obj = -53.409346, rho = 0.182588
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -6.712325, rho = -0.476503
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 93% (93/100) (classification)
Accuracy = 90.1% (901/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.836787
obj = -7.884110, rho = -0.394084
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.792902
obj = -9.181625, rho = -0.351427
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.734280
obj = -10.567227, rho = -0.321615
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.679584
obj = -12.038888, rho = -0.247454
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.603295
obj = -13.603455, rho = -0.209916
nSV = 65, nBSV = 57
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.538055
obj = -15.399745, rho = -0.189940
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.481095
obj = -17.350491, rho = -0.215008
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.420584
obj = -19.538829, rho = -0.190557
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.376633
obj = -22.025656, rho = -0.226231
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.334521
obj = -24.693673, rho = -0.228882
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.297453
obj = -27.660107, rho = -0.193682
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.261424
obj = -30.896177, rho = -0.134467
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.230374
obj = -34.439140, rho = -0.167357
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.201975
obj = -38.208405, rho = -0.158217
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.175060
obj = -42.348705, rho = -0.192268
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.149436
obj = -47.206667, rho = -0.186162
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.131813
obj = -52.864522, rho = -0.200525
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.115885
obj = -58.965067, rho = -0.248208
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.101039
obj = -65.996777, rho = -0.218346
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.671344, rho = -0.134805
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.825994
obj = -7.842488, rho = -0.175771
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -9.152331, rho = -0.223587
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.723151
obj = -10.555928, rho = -0.246950
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.656021
obj = -12.131874, rho = -0.184952
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.606602
obj = -13.880236, rho = -0.124046
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.543518
obj = -15.766483, rho = -0.083052
nSV = 57, nBSV = 49
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.479429
obj = -17.949722, rho = -0.057154
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.438222
obj = -20.399399, rho = -0.026283
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.390715
obj = -23.040362, rho = -0.023195
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.346121
obj = -26.088351, rho = -0.075942
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.309881
obj = -29.342150, rho = -0.122062
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.280302
obj = -32.720317, rho = -0.230111
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.243468
obj = -36.384909, rho = -0.230422
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.211577
obj = -40.502122, rho = -0.240020
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.184898
obj = -45.131356, rho = -0.245945
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.164706
obj = -49.933534, rho = -0.276149
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.142295
obj = -55.116890, rho = -0.339372
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.123437
obj = -60.966953, rho = -0.329302
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 315
nu = 0.107756
obj = -67.298691, rho = -0.292073
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.792516, rho = 0.027684
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 88.9% (889/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -8.032263, rho = -0.070298
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.801807
obj = -9.377138, rho = -0.130539
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.745233
obj = -10.798667, rho = -0.127137
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.684778
obj = -12.354421, rho = -0.200553
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.607726
obj = -14.103628, rho = -0.204073
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.544714
obj = -16.144014, rho = -0.240573
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.489480
obj = -18.477068, rho = -0.209801
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.448296
obj = -21.021677, rho = -0.148300
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.395800
obj = -23.828200, rho = -0.160733
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.352255
obj = -27.102397, rho = -0.200757
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.319063
obj = -30.711663, rho = -0.159062
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.290028
obj = -34.687367, rho = -0.098687
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.258396
obj = -38.794903, rho = -0.159746
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.228855
obj = -43.062249, rho = -0.135997
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.197677
obj = -47.741206, rho = -0.113319
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.174590
obj = -52.855499, rho = -0.151163
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.152155
obj = -58.127485, rho = -0.152263
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.131674
obj = -63.887772, rho = -0.114461
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.119842
obj = -69.194486, rho = 0.098171
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -7.082844, rho = -0.186255
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.891901
obj = -8.262205, rho = -0.184764
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.822560
obj = -9.573391, rho = -0.153622
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.759249
obj = -11.019819, rho = -0.091838
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.688303
obj = -12.668234, rho = -0.078260
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.625052
obj = -14.518251, rho = -0.047212
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.563320
obj = -16.556447, rho = -0.068956
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.510563
obj = -18.777331, rho = -0.087686
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.453027
obj = -21.299083, rho = -0.134671
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.405353
obj = -24.115935, rho = -0.067746
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.357323
obj = -27.335043, rho = -0.089458
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.326373
obj = -30.893536, rho = -0.077487
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.290368
obj = -34.556429, rho = -0.088748
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.259276
obj = -38.661630, rho = -0.177377
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.228420
obj = -42.744194, rho = -0.232573
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*.*
optimization finished, #iter = 315
nu = 0.193441
obj = -47.436483, rho = -0.245820
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.168319
obj = -53.207849, rho = -0.235200
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.151932
obj = -58.947371, rho = -0.234075
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*...*
optimization finished, #iter = 610
nu = 0.129911
obj = -65.412679, rho = -0.272618
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.110482
obj = -73.265800, rho = -0.256500
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.909441
obj = -6.707481, rho = 0.069260
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.873544
obj = -7.769371, rho = -0.101169
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.812264
obj = -8.871279, rho = -0.098195
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.735005
obj = -10.000654, rho = -0.092110
nSV = 76, nBSV = 71
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.650626
obj = -11.214178, rho = -0.088430
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.578288
obj = -12.514727, rho = -0.026551
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.515736
obj = -13.880297, rho = 0.014640
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.444386
obj = -15.305717, rho = -0.022189
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.387591
obj = -16.897623, rho = -0.038887
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.335848
obj = -18.589883, rho = -0.058489
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.287386
obj = -20.529092, rho = -0.066739
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.251593
obj = -22.680722, rho = -0.014152
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.220560
obj = -25.004105, rho = -0.001518
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.193832
obj = -27.133413, rho = 0.046027
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.167487
obj = -29.202599, rho = 0.034416
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.143283
obj = -31.220139, rho = 0.045516
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.121377
obj = -33.200968, rho = 0.042910
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.102221
obj = -34.871494, rho = 0.024987
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.083881
obj = -36.683362, rho = 0.039383
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.068701
obj = -38.653713, rho = 0.079921
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.930705
obj = -6.799940, rho = -0.174250
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.910631, rho = -0.136595
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.801773
obj = -9.142384, rho = -0.163776
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.740000
obj = -10.470945, rho = -0.265226
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.663648
obj = -11.911056, rho = -0.281215
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.605960
obj = -13.443287, rho = -0.254451
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.542820
obj = -15.075189, rho = -0.285383
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.476828
obj = -16.848275, rho = -0.332988
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.418022
obj = -18.814728, rho = -0.317668
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.366389
obj = -20.959629, rho = -0.327964
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.324074
obj = -23.359066, rho = -0.336321
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.279313
obj = -25.967056, rho = -0.335876
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.243820
obj = -28.928876, rho = -0.330620
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.212764
obj = -32.389282, rho = -0.303965
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.186215
obj = -36.293199, rho = -0.309454
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.166373
obj = -40.658013, rho = -0.225167
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.144642
obj = -45.453918, rho = -0.230528
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.128853
obj = -50.799916, rho = -0.193973
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.110201
obj = -56.711097, rho = -0.167401
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.096842
obj = -63.441346, rho = -0.162823
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -6.525518, rho = -0.418353
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 91% (91/100) (classification)
Accuracy = 91% (910/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.656474, rho = -0.340705
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.779215
obj = -8.865590, rho = -0.250596
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.716154
obj = -10.160703, rho = -0.214557
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.637417
obj = -11.600073, rho = -0.205042
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.569743
obj = -13.275722, rho = -0.239232
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.508457
obj = -15.225823, rho = -0.211487
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.471757
obj = -17.361849, rho = -0.152636
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.422608
obj = -19.630143, rho = -0.189406
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.368894
obj = -22.260252, rho = -0.226017
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.330701
obj = -25.351888, rho = -0.166881
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.303441
obj = -28.605492, rho = -0.159535
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.272127
obj = -32.047930, rho = -0.076012
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.238948
obj = -35.701705, rho = -0.139295
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.207925
obj = -39.691574, rho = -0.187954
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.180137
obj = -44.195512, rho = -0.205155
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.155720
obj = -49.519570, rho = -0.228928
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.135046
obj = -55.891495, rho = -0.251225
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.121636
obj = -63.154606, rho = -0.389098
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.104943
obj = -71.348773, rho = -0.432852
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.618771, rho = -0.372644
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 90.6% (906/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.856349
obj = -7.650155, rho = -0.273713
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -8.769121, rho = -0.236047
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.719617
obj = -10.010796, rho = -0.295855
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.632193
obj = -11.346514, rho = -0.291103
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.565293
obj = -12.886136, rho = -0.258616
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.512593
obj = -14.574500, rho = -0.274821
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.463144
obj = -16.297506, rho = -0.264524
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.406504
obj = -18.173154, rho = -0.306328
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.360185
obj = -20.166915, rho = -0.287732
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.309327
obj = -22.397599, rho = -0.264142
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.266768
obj = -25.009660, rho = -0.261294
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.239028
obj = -27.811204, rho = -0.225032
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.209072
obj = -30.845610, rho = -0.287498
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.183199
obj = -34.001795, rho = -0.268370
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.155410
obj = -37.538466, rho = -0.265568
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.136002
obj = -41.638581, rho = -0.298788
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.119517
obj = -45.866218, rho = -0.409556
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 193
nu = 0.102781
obj = -50.496239, rho = -0.450725
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.088715
obj = -55.712163, rho = -0.472821
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -7.004354, rho = 0.190696
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 91% (91/100) (classification)
Accuracy = 89.9% (899/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -8.289188, rho = 0.079497
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.834920
obj = -9.682317, rho = -0.086908
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.771151
obj = -11.177137, rho = -0.163852
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.703649
obj = -12.771180, rho = -0.125337
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.636422
obj = -14.557330, rho = -0.050743
nSV = 68, nBSV = 60
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.574544
obj = -16.543659, rho = -0.004953
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.515796
obj = -18.727461, rho = -0.007254
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.452528
obj = -21.161040, rho = -0.004635
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.397962
obj = -24.029929, rho = -0.016521
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.360000
obj = -27.289204, rho = -0.014130
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.321639
obj = -30.799080, rho = 0.005159
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.281131
obj = -34.864341, rho = 0.062684
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.250398
obj = -39.681663, rho = 0.128325
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.230261
obj = -44.822600, rho = 0.180445
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.209394
obj = -50.014265, rho = 0.109618
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.183495
obj = -55.022549, rho = 0.118279
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.159600
obj = -60.218989, rho = 0.227702
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 331
nu = 0.134281
obj = -66.168646, rho = 0.253874
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.116989
obj = -73.098167, rho = 0.292783
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -6.447987, rho = -0.622817
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 76% (76/100) (classification)
Accuracy = 70.2% (702/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -7.743977, rho = -0.519365
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 89% (89/100) (classification)
Accuracy = 86.1% (861/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.774790
obj = -9.101389, rho = -0.404189
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.720000
obj = -10.538688, rho = -0.390609
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.669463
obj = -12.088118, rho = -0.318155
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.613597
obj = -13.706075, rho = -0.331405
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.543814
obj = -15.479258, rho = -0.359130
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.488093
obj = -17.415192, rho = -0.344030
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.431646
obj = -19.510795, rho = -0.352231
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.381405
obj = -21.830158, rho = -0.317572
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.336056
obj = -24.186848, rho = -0.267853
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.293346
obj = -26.796462, rho = -0.281093
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.250281
obj = -29.856555, rho = -0.263523
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.219511
obj = -33.466956, rho = -0.245107
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.194819
obj = -37.362331, rho = -0.228542
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.176629
obj = -41.416880, rho = -0.302672
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.148805
obj = -45.640926, rho = -0.310102
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.129961
obj = -50.579908, rho = -0.351551
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.113487
obj = -55.975977, rho = -0.449311
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.100737
obj = -61.349538, rho = -0.623829
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -7.561058, rho = 0.245710
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.941215
obj = -8.929947, rho = 0.123229
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.893958
obj = -10.404808, rho = 0.077486
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.826614
obj = -12.004148, rho = 0.057023
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.758437
obj = -13.750414, rho = -0.013421
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.681546
obj = -15.720704, rho = -0.024071
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.615138
obj = -17.869317, rho = 0.007623
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.549011
obj = -20.298883, rho = 0.022752
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.489007
obj = -23.037745, rho = -0.039354
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.440432
obj = -26.164409, rho = -0.051699
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.399090
obj = -29.457487, rho = -0.003467
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.348440
obj = -33.098104, rho = -0.022581
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.305066
obj = -37.250440, rho = -0.034209
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.274158
obj = -41.952435, rho = -0.016027
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.241574
obj = -46.912002, rho = -0.021182
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.210429
obj = -52.741160, rho = -0.050069
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.189640
obj = -58.971637, rho = -0.023383
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.169264
obj = -65.389389, rho = 0.004245
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.143834
obj = -72.571580, rho = -0.001747
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 313
nu = 0.126010
obj = -80.616508, rho = -0.043257
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.887032
obj = -6.653603, rho = -0.050877
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 93% (93/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.830682
obj = -7.803594, rho = -0.130128
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.763218
obj = -9.131031, rho = -0.179110
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.720000
obj = -10.615969, rho = -0.130017
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.670515
obj = -12.200195, rho = -0.070400
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.607147
obj = -13.930511, rho = -0.070717
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.542877
obj = -15.854986, rho = -0.103363
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.490384
obj = -17.975706, rho = -0.163569
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.438812
obj = -20.308712, rho = -0.168399
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.396104
obj = -22.788241, rho = -0.140076
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.351346
obj = -25.406015, rho = -0.127056
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.305776
obj = -28.280500, rho = -0.120680
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.271819
obj = -31.380112, rho = -0.095169
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.242178
obj = -34.461381, rho = -0.137954
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.210387
obj = -37.367093, rho = -0.267142
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.176159
obj = -40.578261, rho = -0.263534
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 241
nu = 0.152523
obj = -43.979556, rho = -0.332550
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 338
nu = 0.128701
obj = -47.581311, rho = -0.375586
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 337
nu = 0.110659
obj = -51.340673, rho = -0.462860
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.095656
obj = -55.298753, rho = -0.480011
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.923881, rho = 0.282010
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 84% (84/100) (classification)
Accuracy = 83.8% (838/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -8.237122, rho = 0.085083
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 95% (95/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.808222
obj = -9.665422, rho = 0.118183
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.750918
obj = -11.249711, rho = 0.087581
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.698035
obj = -13.017089, rho = 0.054484
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.626263
obj = -15.033744, rho = 0.036447
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.580000
obj = -17.323064, rho = 0.011263
nSV = 58, nBSV = 58
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.530869
obj = -19.747425, rho = 0.001335
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.475677
obj = -22.440659, rho = -0.027742
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.427281
obj = -25.513237, rho = 0.045675
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.378902
obj = -28.908865, rho = 0.065673
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.341481
obj = -32.688734, rho = 0.103802
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.302902
obj = -36.806146, rho = 0.157258
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.263846
obj = -41.538999, rho = 0.208546
nSV = 32, nBSV = 22
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.232146
obj = -47.166486, rho = 0.238511
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.203360
obj = -53.931208, rho = 0.238019
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.180737
obj = -62.037787, rho = 0.255409
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.164094
obj = -71.773471, rho = 0.302918
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.152260
obj = -82.550627, rho = 0.344180
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.140987
obj = -93.563541, rho = 0.369680
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.896368
obj = -6.913463, rho = -0.326232
nSV = 92, nBSV = 87
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.855611
obj = -8.183533, rho = -0.271927
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.800000
obj = -9.591564, rho = -0.200563
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.754234
obj = -11.177746, rho = -0.137357
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.686899
obj = -12.913748, rho = -0.092622
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.629148
obj = -14.909568, rho = -0.046884
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.580374
obj = -17.065950, rho = -0.057156
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.524463
obj = -19.400248, rho = -0.115319
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.465445
obj = -22.087421, rho = -0.126725
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.418744
obj = -25.073097, rho = -0.175225
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.370814
obj = -28.498365, rho = -0.172334
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.331008
obj = -32.316470, rho = -0.242146
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.295107
obj = -36.639741, rho = -0.265557
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.266990
obj = -41.420134, rho = -0.171784
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.235631
obj = -46.728913, rho = -0.168747
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.208079
obj = -52.643246, rho = -0.154376
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.182212
obj = -59.654893, rho = -0.174595
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 180
nu = 0.163091
obj = -67.732347, rho = -0.124278
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*
optimization finished, #iter = 347
nu = 0.141378
obj = -77.256780, rho = -0.095590
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.126046
obj = -88.803247, rho = -0.031062
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.838616
obj = -6.406631, rho = -0.485501
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 92% (92/100) (classification)
Accuracy = 86.7% (867/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.788318
obj = -7.551983, rho = -0.456006
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 95% (95/100) (classification)
Accuracy = 90% (900/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.735864
obj = -8.856471, rho = -0.449403
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.682937
obj = -10.346828, rho = -0.406496
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 95% (95/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.624285
obj = -12.058605, rho = -0.361604
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 95% (95/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.577295
obj = -14.062672, rho = -0.389716
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 96% (96/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.521741
obj = -16.400619, rho = -0.424226
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.475317
obj = -19.184507, rho = -0.423521
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.444261
obj = -22.331378, rho = -0.362243
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 95% (95/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.400763
obj = -25.955541, rho = -0.365644
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 95% (95/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.374338
obj = -29.986423, rho = -0.309987
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.344521
obj = -34.484939, rho = -0.373324
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.311035
obj = -39.470315, rho = -0.409691
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.280000
obj = -45.281719, rho = -0.366926
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.250641
obj = -51.593788, rho = -0.341792
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.226804
obj = -59.076035, rho = -0.316699
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.208324
obj = -67.312090, rho = -0.352531
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.186928
obj = -76.141265, rho = -0.458504
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 203
nu = 0.165463
obj = -85.508306, rho = -0.557781
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 471
nu = 0.146545
obj = -95.813264, rho = -0.598024
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.937162
obj = -7.151930, rho = -0.127041
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.888952
obj = -8.431412, rho = -0.128725
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.826203
obj = -9.859592, rho = -0.126640
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.767162
obj = -11.488366, rho = -0.106768
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700000
obj = -13.346713, rho = -0.108660
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.651107
obj = -15.440392, rho = -0.103820
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.594949
obj = -17.755051, rho = -0.112222
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.538342
obj = -20.375194, rho = -0.106477
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.488945
obj = -23.233338, rho = -0.151838
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.445906
obj = -26.356276, rho = -0.075199
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.396771
obj = -29.736164, rho = -0.076092
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.352205
obj = -33.570727, rho = -0.111534
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.308526
obj = -37.772450, rho = -0.136832
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.277653
obj = -42.491253, rho = -0.224077
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.241654
obj = -47.790721, rho = -0.191437
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.212193
obj = -53.950372, rho = -0.163036
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.185762
obj = -61.189549, rho = -0.135774
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.**.*
optimization finished, #iter = 164
nu = 0.170909
obj = -69.334030, rho = -0.285567
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.**.*
optimization finished, #iter = 199
nu = 0.146814
obj = -78.317874, rho = -0.278052
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.128113
obj = -89.318112, rho = -0.299573
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.931943
obj = -7.032448, rho = -0.026985
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 90% (90/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.872231
obj = -8.262053, rho = -0.050329
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 93% (93/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820925
obj = -9.653761, rho = -0.078026
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.764877
obj = -11.158775, rho = -0.103774
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.692785
obj = -12.840173, rho = -0.129929
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.631932
obj = -14.752701, rho = -0.141616
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.568550
obj = -16.897902, rho = -0.162973
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.517075
obj = -19.318989, rho = -0.148429
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.470602
obj = -21.978411, rho = -0.205981
nSV = 48, nBSV = 46
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.422309
obj = -24.763516, rho = -0.148124
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.371766
obj = -27.999926, rho = -0.176419
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.331334
obj = -31.611198, rho = -0.172362
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.297026
obj = -35.588997, rho = -0.135558
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.265376
obj = -39.756209, rho = -0.190114
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.231398
obj = -44.210537, rho = -0.203261
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.205077
obj = -49.207827, rho = -0.169796
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.176241
obj = -54.510551, rho = -0.190492
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.154677
obj = -60.645308, rho = -0.171307
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*
optimization finished, #iter = 295
nu = 0.134402
obj = -67.050432, rho = -0.145733
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.114209
obj = -74.725216, rho = -0.149273
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.893432
obj = -6.639570, rho = -0.061369
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 91% (91/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.838533
obj = -7.739476, rho = -0.140265
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.765210
obj = -8.988347, rho = -0.135056
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.709386
obj = -10.386646, rho = -0.140436
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.644371
obj = -11.978818, rho = -0.099217
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.589330
obj = -13.718089, rho = -0.074625
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.522615
obj = -15.758759, rho = -0.045772
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.471177
obj = -18.154634, rho = -0.067830
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.426237
obj = -20.923930, rho = -0.054047
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.388683
obj = -24.061703, rho = 0.000019
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.351532
obj = -27.620485, rho = 0.040380
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.319151
obj = -31.532406, rho = 0.013713
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.290837
obj = -35.946111, rho = 0.042898
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.256662
obj = -40.808432, rho = 0.048659
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.232470
obj = -46.337295, rho = 0.101059
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.209449
obj = -52.300903, rho = 0.085695
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.184127
obj = -58.745286, rho = 0.096430
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.160327
obj = -66.251817, rho = 0.098644
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.141921
obj = -75.088808, rho = 0.124076
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.129090
obj = -84.553787, rho = 0.183008
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.920000
obj = -6.794953, rho = -0.342450
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 93% (930/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.876780
obj = -7.854116, rho = -0.254989
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.796795
obj = -9.035854, rho = -0.236895
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.743835
obj = -10.307079, rho = -0.241113
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.661259
obj = -11.637984, rho = -0.239484
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.593518
obj = -13.073355, rho = -0.268154
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.529497
obj = -14.597264, rho = -0.241803
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.461986
obj = -16.279274, rho = -0.282360
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.402820
obj = -18.161440, rho = -0.289724
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.349309
obj = -20.353737, rho = -0.328000
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.307243
obj = -22.886155, rho = -0.306099
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.265070
obj = -25.865392, rho = -0.310997
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.232906
obj = -29.443845, rho = -0.324495
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.206368
obj = -33.736463, rho = -0.289382
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.187984
obj = -38.637411, rho = -0.356453
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.166260
obj = -44.322721, rho = -0.398255
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.149706
obj = -51.008064, rho = -0.452348
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.137656
obj = -58.512144, rho = -0.573911
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.127295
obj = -66.534902, rho = -0.919197
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.114677
obj = -74.520186, rho = -1.126489
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.594750, rho = -0.130953
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 93% (93/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -7.731764, rho = -0.197593
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 95% (95/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760000
obj = -9.041557, rho = -0.175274
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.702616
obj = -10.529302, rho = -0.139432
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.660000
obj = -12.175443, rho = -0.086986
nSV = 66, nBSV = 66
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.612183
obj = -13.900348, rho = -0.032435
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.540000
obj = -15.824069, rho = -0.069713
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.486382
obj = -17.959743, rho = -0.074077
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.428820
obj = -20.440697, rho = -0.103410
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.389540
obj = -23.267027, rho = -0.163514
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.358725
obj = -26.231425, rho = -0.159323
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.313343
obj = -29.386262, rho = -0.189465
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.276004
obj = -32.833465, rho = -0.170514
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.241735
obj = -36.716729, rho = -0.173729
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.211861
obj = -41.082818, rho = -0.250415
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.190470
obj = -45.941089, rho = -0.303190
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.170942
obj = -50.460506, rho = -0.169348
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.148169
obj = -54.900964, rho = -0.159807
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.124737
obj = -59.673697, rho = -0.148955
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.105118
obj = -65.323069, rho = -0.157373
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -7.061157, rho = -0.120810
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.264669, rho = -0.161033
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.824956
obj = -9.603609, rho = -0.160984
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.757897
obj = -11.078131, rho = -0.107538
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.679593
obj = -12.791372, rho = -0.140695
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.621054
obj = -14.801206, rho = -0.177714
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.569133
obj = -17.035188, rho = -0.237111
nSV = 58, nBSV = 56
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.515816
obj = -19.493567, rho = -0.246482
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.461556
obj = -22.305554, rho = -0.218606
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.411976
obj = -25.598820, rho = -0.198004
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.367454
obj = -29.475717, rho = -0.220348
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.338298
obj = -33.933189, rho = -0.205852
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.307397
obj = -38.832110, rho = -0.139711
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.280346
obj = -44.137921, rho = -0.176386
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.248991
obj = -50.159701, rho = -0.219216
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.226001
obj = -56.948515, rho = -0.253856
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.195954
obj = -64.383118, rho = -0.319025
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.182341
obj = -72.357317, rho = -0.562067
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.156278
obj = -80.953558, rho = -0.610579
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.136854
obj = -91.112227, rho = -0.676216
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.939453
obj = -7.086324, rho = -0.270400
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.893167
obj = -8.309115, rho = -0.296348
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.835984
obj = -9.636974, rho = -0.243511
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.776881
obj = -11.075586, rho = -0.175056
nSV = 81, nBSV = 74
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.703331
obj = -12.654206, rho = -0.145708
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.621797
obj = -14.443039, rho = -0.129443
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.563496
obj = -16.463739, rho = -0.105498
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.505876
obj = -18.712594, rho = -0.095821
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.449866
obj = -21.235989, rho = -0.085151
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.399134
obj = -24.129062, rho = -0.075428
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.360636
obj = -27.442820, rho = -0.051835
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.320666
obj = -31.063515, rho = 0.004824
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.288130
obj = -35.158912, rho = -0.061390
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.252600
obj = -39.671346, rho = -0.055250
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.227539
obj = -44.586454, rho = -0.121089
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.197741
obj = -50.350845, rho = -0.040424
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.179503
obj = -56.532852, rho = 0.142685
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.159227
obj = -63.165462, rho = 0.146838
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.141010
obj = -70.170278, rho = 0.074861
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.120596
obj = -77.855252, rho = -0.010677
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.828935, rho = -0.315330
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.867189
obj = -7.949340, rho = -0.270598
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.807653
obj = -9.175853, rho = -0.244488
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.725499
obj = -10.551576, rho = -0.229458
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.677271
obj = -12.080113, rho = -0.159899
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.607707
obj = -13.685067, rho = -0.164056
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.539462
obj = -15.484273, rho = -0.120459
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.480745
obj = -17.519343, rho = -0.073294
nSV = 50, nBSV = 47
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.430675
obj = -19.683828, rho = -0.100995
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.383794
obj = -22.054952, rho = -0.156799
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.332236
obj = -24.696451, rho = -0.152865
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.295212
obj = -27.771419, rho = -0.170657
nSV = 31, nBSV = 28
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.261958
obj = -30.989726, rho = -0.196091
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.228349
obj = -34.556286, rho = -0.206801
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.201987
obj = -38.600716, rho = -0.243820
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.175128
obj = -42.964810, rho = -0.252769
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.151823
obj = -48.049844, rho = -0.215784
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.136994
obj = -53.663558, rho = -0.205009
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.118026
obj = -59.586848, rho = -0.162155
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.101702
obj = -66.604726, rho = -0.127163
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.887389
obj = -6.570869, rho = -0.177235
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.835758
obj = -7.654915, rho = -0.228240
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.767518
obj = -8.856052, rho = -0.259318
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.700000
obj = -10.197062, rho = -0.188377
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.638540
obj = -11.679883, rho = -0.148309
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.568444
obj = -13.397923, rho = -0.153199
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.526753
obj = -15.317931, rho = -0.271212
nSV = 54, nBSV = 52
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.468475
obj = -17.389904, rho = -0.259913
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.411102
obj = -19.856230, rho = -0.276935
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.374937
obj = -22.695543, rho = -0.255276
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.333306
obj = -25.781717, rho = -0.216244
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.295302
obj = -29.456986, rho = -0.213715
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.266295
obj = -33.703433, rho = -0.184688
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.239245
obj = -38.474806, rho = -0.171933
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.217746
obj = -43.894941, rho = -0.160230
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.193831
obj = -49.736587, rho = -0.150922
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.171422
obj = -56.542220, rho = -0.131918
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.154175
obj = -64.282256, rho = -0.158832
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.140735
obj = -72.681160, rho = -0.235091
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.130439
obj = -80.664511, rho = -0.351326
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.934766
obj = -6.775634, rho = -0.130507
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.871698
obj = -7.839383, rho = -0.162386
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.795456
obj = -9.000614, rho = -0.133957
nSV = 82, nBSV = 77
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.728869
obj = -10.299244, rho = -0.110504
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.656297
obj = -11.708742, rho = -0.085120
nSV = 69, nBSV = 62
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.583062
obj = -13.326932, rho = -0.092751
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.520000
obj = -15.149869, rho = -0.094584
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.481883
obj = -17.082756, rho = -0.005594
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.424136
obj = -19.120104, rho = -0.002343
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.367720
obj = -21.445740, rho = -0.012205
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.323571
obj = -24.062974, rho = 0.003758
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.285133
obj = -27.145875, rho = 0.044668
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.252373
obj = -30.426526, rho = 0.059979
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.223681
obj = -34.186309, rho = 0.074472
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.200784
obj = -38.040142, rho = 0.134920
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.183179
obj = -41.831967, rho = 0.091787
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.156560
obj = -45.130694, rho = 0.058833
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.131992
obj = -48.836157, rho = 0.024481
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.111540
obj = -52.980115, rho = -0.002057
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.098168
obj = -56.836515, rho = -0.087564
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.952090
obj = -7.090762, rho = -0.131745
nSV = 96, nBSV = 93
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -8.282298, rho = -0.077082
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.826044
obj = -9.584804, rho = -0.047326
nSV = 86, nBSV = 79
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.754833
obj = -11.079273, rho = -0.019365
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.697798
obj = -12.741046, rho = -0.010781
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.629490
obj = -14.547337, rho = 0.028245
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.562793
obj = -16.562924, rho = 0.078749
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.511910
obj = -18.876222, rho = 0.115710
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.453544
obj = -21.447531, rho = 0.154184
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.402823
obj = -24.434015, rho = 0.197393
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.360207
obj = -27.785752, rho = 0.192431
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.318401
obj = -31.642574, rho = 0.132977
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.287423
obj = -36.132686, rho = 0.091244
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.264735
obj = -40.890761, rho = 0.220425
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.236416
obj = -45.885398, rho = 0.220151
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.208865
obj = -51.342856, rho = 0.254070
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.184067
obj = -57.526082, rho = 0.316227
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.158400
obj = -64.292663, rho = 0.338905
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.138912
obj = -72.374744, rho = 0.250397
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.122763
obj = -81.551742, rho = 0.160151
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -7.088597, rho = -0.390137
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 92% (92/100) (classification)
Accuracy = 90.1% (901/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.884383
obj = -8.341379, rho = -0.289967
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.842300
obj = -9.682072, rho = -0.228468
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.774625
obj = -11.130065, rho = -0.183477
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.705439
obj = -12.695908, rho = -0.141101
nSV = 74, nBSV = 68
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.627516
obj = -14.458392, rho = -0.175269
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.558160
obj = -16.480983, rho = -0.161294
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.517656
obj = -18.650850, rho = -0.095100
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.450117
obj = -21.042209, rho = -0.112704
nSV = 50, nBSV = 41
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.401347
obj = -23.879986, rho = -0.156260
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.356253
obj = -27.073871, rho = -0.193720
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.321633
obj = -30.500341, rho = -0.161666
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.289424
obj = -33.944518, rho = -0.218767
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 265
nu = 0.251696
obj = -37.821896, rho = -0.245248
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.218945
obj = -42.232641, rho = -0.344927
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.200752
obj = -46.782321, rho = -0.414637
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.172448
obj = -51.183653, rho = -0.439901
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.146837
obj = -56.126919, rho = -0.469041
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 187
nu = 0.129447
obj = -61.414030, rho = -0.383975
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.111611
obj = -66.554921, rho = -0.426080
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.840000
obj = -6.296594, rho = 0.162618
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 89.9% (899/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -7.335552, rho = 0.059606
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.749992
obj = -8.444394, rho = 0.044880
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.680000
obj = -9.640598, rho = -0.024119
nSV = 68, nBSV = 68
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.612317
obj = -10.940296, rho = -0.026722
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.545248
obj = -12.409111, rho = -0.030023
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.493489
obj = -14.057288, rho = 0.009040
nSV = 50, nBSV = 48
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.442258
obj = -15.791379, rho = -0.035047
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.394847
obj = -17.605438, rho = -0.084502
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.343556
obj = -19.547009, rho = -0.112276
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.302030
obj = -21.734128, rho = -0.124335
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.258683
obj = -24.192499, rho = -0.128523
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.222611
obj = -27.205643, rho = -0.134340
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.195685
obj = -30.778395, rho = -0.143809
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.172521
obj = -34.855953, rho = -0.161099
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.154019
obj = -39.640945, rho = -0.203283
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.134877
obj = -45.162757, rho = -0.230513
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.120436
obj = -51.942539, rho = -0.277427
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.107861
obj = -59.562023, rho = -0.313446
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.096023
obj = -68.847354, rho = -0.312090
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.925643
obj = -7.126138, rho = 0.000462
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 94% (94/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.893107
obj = -8.397106, rho = -0.144726
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.830929
obj = -9.798907, rho = -0.144827
nSV = 87, nBSV = 81
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.772865
obj = -11.344858, rho = -0.233754
nSV = 80, nBSV = 75
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.720000
obj = -13.075034, rho = -0.250090
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.649609
obj = -14.900978, rho = -0.198952
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.600000
obj = -16.867815, rho = -0.212325
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.527773
obj = -18.935255, rho = -0.219271
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.466678
obj = -21.301543, rho = -0.178116
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.410019
obj = -23.878993, rho = -0.168353
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.365659
obj = -26.660549, rho = -0.122103
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.319154
obj = -29.770685, rho = -0.111343
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.286327
obj = -33.117637, rho = -0.068283
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.246688
obj = -36.653774, rho = -0.089288
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.218211
obj = -40.400803, rho = -0.122440
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.188828
obj = -44.408153, rho = -0.115297
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 181
nu = 0.161874
obj = -48.923173, rho = -0.063712
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.138623
obj = -54.070350, rho = -0.022392
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.120459
obj = -59.764300, rho = -0.013771
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.103872
obj = -66.215897, rho = -0.004868
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.918881
obj = -6.539856, rho = -0.205878
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.540097, rho = -0.203739
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.767207
obj = -8.660489, rho = -0.213974
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.700769
obj = -9.891367, rho = -0.162300
nSV = 75, nBSV = 68
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.636764
obj = -11.220649, rho = -0.106352
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.576503
obj = -12.626519, rho = -0.089598
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.503436
obj = -14.161994, rho = -0.084422
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.443838
obj = -15.923425, rho = -0.140994
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.386105
obj = -17.925249, rho = -0.145794
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.342297
obj = -20.284717, rho = -0.149044
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.301608
obj = -22.907114, rho = -0.173212
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.267513
obj = -26.008318, rho = -0.165417
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.237968
obj = -29.489160, rho = -0.134803
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.215629
obj = -33.399500, rho = -0.108065
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.190115
obj = -37.520177, rho = -0.077128
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.170421
obj = -42.293340, rho = -0.079631
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.153560
obj = -47.170305, rho = -0.114139
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.138796
obj = -51.832802, rho = -0.141688
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.124361
obj = -55.578130, rho = -0.118276
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.105669
obj = -58.619489, rho = -0.130985
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -5.990129, rho = -0.010025
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.769312
obj = -6.919794, rho = -0.020040
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.701576
obj = -7.943118, rho = 0.018953
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.643816
obj = -9.092081, rho = 0.039045
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.579310
obj = -10.308236, rho = -0.005695
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.520407
obj = -11.680981, rho = 0.017902
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.456994
obj = -13.234517, rho = 0.027081
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.424026
obj = -14.879838, rho = -0.011505
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.377192
obj = -16.492221, rho = -0.062299
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.328192
obj = -18.247522, rho = -0.062338
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.285501
obj = -20.068189, rho = -0.088944
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.244449
obj = -22.136061, rho = -0.103275
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.219386
obj = -24.440547, rho = -0.185689
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.190737
obj = -26.431523, rho = -0.202343
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.160889
obj = -28.610316, rho = -0.161341
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.142161
obj = -30.771764, rho = -0.126997
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.119378
obj = -32.541647, rho = -0.094916
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.099293
obj = -34.399224, rho = -0.063361
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.083159
obj = -36.083071, rho = -0.049827
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.068536
obj = -37.801294, rho = -0.071139
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -6.322965, rho = 0.469978
nSV = 74, nBSV = 74
Total nSV = 74
Accuracy = 66% (66/100) (classification)
Accuracy = 57.9% (579/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -7.680771, rho = 0.324606
nSV = 74, nBSV = 74
Total nSV = 74
Accuracy = 77% (77/100) (classification)
Accuracy = 72.6% (726/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.740000
obj = -9.176184, rho = 0.139363
nSV = 74, nBSV = 74
Total nSV = 74
Accuracy = 92% (92/100) (classification)
Accuracy = 87.9% (879/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.720000
obj = -10.723485, rho = 0.004604
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.666233
obj = -12.409818, rho = -0.009447
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.613477
obj = -14.234895, rho = -0.038192
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.562573
obj = -16.184471, rho = -0.103344
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.510178
obj = -18.239410, rho = -0.118873
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.453659
obj = -20.407456, rho = -0.129571
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.395797
obj = -22.762702, rho = -0.062323
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.350506
obj = -25.400857, rho = -0.023287
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.306405
obj = -28.246542, rho = -0.015248
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.265421
obj = -31.504824, rho = 0.033153
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.235231
obj = -35.143476, rho = 0.032199
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.207402
obj = -38.963439, rho = -0.003186
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.179812
obj = -43.090781, rho = -0.072789
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.158125
obj = -47.502669, rho = -0.043432
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.135530
obj = -52.228932, rho = -0.014474
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.115949
obj = -57.592570, rho = 0.043131
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.104857
obj = -63.324693, rho = -0.131062
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.259324, rho = -0.456752
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 87% (87/100) (classification)
Accuracy = 83% (830/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -8.572105, rho = -0.307752
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.854041
obj = -9.944883, rho = -0.237805
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.792110
obj = -11.456319, rho = -0.211068
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.734723
obj = -13.066987, rho = -0.217740
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.660042
obj = -14.739703, rho = -0.174038
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.590777
obj = -16.543123, rho = -0.170803
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.520193
obj = -18.590050, rho = -0.153711
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.458567
obj = -20.803481, rho = -0.187257
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.401442
obj = -23.311302, rho = -0.198147
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.351907
obj = -26.176703, rho = -0.289083
nSV = 40, nBSV = 30
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.306547
obj = -29.533043, rho = -0.323149
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.270177
obj = -33.377292, rho = -0.393603
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.239303
obj = -37.844365, rho = -0.359955
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.219842
obj = -42.813487, rho = -0.324195
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.194265
obj = -47.871878, rho = -0.252087
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.169593
obj = -53.462649, rho = -0.237487
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.148284
obj = -60.063266, rho = -0.245286
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.130785
obj = -67.640663, rho = -0.224470
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.113547
obj = -76.212453, rho = -0.218968
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.911331
obj = -6.711400, rho = -0.363908
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 95% (95/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.846570
obj = -7.819089, rho = -0.327947
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.780000
obj = -9.075498, rho = -0.324362
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.731160
obj = -10.426487, rho = -0.310930
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.668870
obj = -11.862859, rho = -0.262667
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.599287
obj = -13.415912, rho = -0.251705
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.540000
obj = -15.121644, rho = -0.211632
nSV = 55, nBSV = 53
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.480000
obj = -16.842685, rho = -0.169239
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.423882
obj = -18.687284, rho = -0.128898
nSV = 46, nBSV = 37
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.368256
obj = -20.750455, rho = -0.078341
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.327110
obj = -22.806051, rho = -0.114378
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.282525
obj = -25.091541, rho = -0.050540
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.245204
obj = -27.517399, rho = -0.039964
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.209666
obj = -30.008382, rho = -0.041211
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.178022
obj = -32.959477, rho = -0.038395
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.151819
obj = -36.302250, rho = -0.046567
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.131909
obj = -40.153326, rho = -0.027853
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.114517
obj = -44.329539, rho = 0.029359
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.101211
obj = -48.757935, rho = -0.014820
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.089409
obj = -52.844033, rho = -0.142716
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.893702
obj = -6.329326, rho = -0.103699
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.822419
obj = -7.264211, rho = -0.146958
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.755717
obj = -8.290340, rho = -0.164680
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.682212
obj = -9.373712, rho = -0.189992
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.607412
obj = -10.565210, rho = -0.153734
nSV = 64, nBSV = 57
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.549708
obj = -11.828321, rho = -0.107890
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.474887
obj = -13.181515, rho = -0.098439
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.416844
obj = -14.753134, rho = -0.090000
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.364283
obj = -16.534715, rho = -0.071628
nSV = 38, nBSV = 35
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.326385
obj = -18.450575, rho = -0.106349
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.284523
obj = -20.471099, rho = -0.086645
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.251129
obj = -22.701320, rho = -0.094882
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.218244
obj = -24.964333, rho = -0.125677
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.185048
obj = -27.591457, rho = -0.108533
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.158450
obj = -30.803942, rho = -0.130104
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.139013
obj = -34.518064, rho = -0.181186
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.122898
obj = -38.623529, rho = -0.264687
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.109824
obj = -43.112777, rho = -0.165680
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.094247
obj = -47.994888, rho = -0.118388
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.083795
obj = -53.685246, rho = 0.010303
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.933990
obj = -6.959753, rho = -0.065112
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.138305, rho = -0.029199
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.813828
obj = -9.446342, rho = -0.001285
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.743122
obj = -10.912860, rho = -0.017788
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.674610
obj = -12.564739, rho = -0.009713
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.620000
obj = -14.419391, rho = -0.071163
nSV = 63, nBSV = 61
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.561388
obj = -16.424451, rho = -0.087030
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.515070
obj = -18.635881, rho = -0.115964
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.456092
obj = -20.970220, rho = -0.177282
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.400000
obj = -23.638316, rho = -0.183533
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.347768
obj = -26.795750, rho = -0.165639
nSV = 40, nBSV = 31
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.310630
obj = -30.522479, rho = -0.218971
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.273839
obj = -34.907099, rho = -0.191470
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.242350
obj = -40.091492, rho = -0.180483
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.219410
obj = -46.284609, rho = -0.206960
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.194113
obj = -53.644605, rho = -0.220158
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.176161
obj = -62.569724, rho = -0.214955
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.165099
obj = -72.830886, rho = -0.203109
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.151878
obj = -84.048566, rho = -0.162976
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.137824
obj = -96.538402, rho = -0.168236
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.800000
obj = -6.330052, rho = 0.352331
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 78% (78/100) (classification)
Accuracy = 72.2% (722/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -7.482578, rho = 0.174692
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 92% (92/100) (classification)
Accuracy = 88.9% (889/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.774549
obj = -8.633751, rho = 0.021553
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.701199
obj = -9.807090, rho = -0.002268
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.630119
obj = -11.094323, rho = -0.052074
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.561916
obj = -12.541491, rho = -0.047744
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.503589
obj = -14.126444, rho = -0.028399
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.446449
obj = -15.836142, rho = -0.018988
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.389497
obj = -17.729991, rho = -0.021331
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.359157
obj = -19.756730, rho = -0.166537
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.307240
obj = -21.753298, rho = -0.179179
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.266152
obj = -24.008930, rho = -0.212261
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.228007
obj = -26.601666, rho = -0.221698
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.197247
obj = -29.605980, rho = -0.246949
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.173602
obj = -33.011641, rho = -0.328332
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.149523
obj = -36.839062, rho = -0.315158
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.133298
obj = -41.163241, rho = -0.301900
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.121537
obj = -45.150729, rho = -0.306922
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.107758
obj = -48.315094, rho = -0.432685
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.089878
obj = -51.473177, rho = -0.379835
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -7.069220, rho = -0.260877
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.892569
obj = -8.266451, rho = -0.236033
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -9.548045, rho = -0.115981
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.754935
obj = -10.933394, rho = -0.104396
nSV = 79, nBSV = 73
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.683440
obj = -12.559127, rho = -0.082397
nSV = 70, nBSV = 68
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.630662
obj = -14.279501, rho = -0.075972
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.560902
obj = -16.186294, rho = -0.117925
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.497809
obj = -18.368808, rho = -0.180446
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.441157
obj = -20.871356, rho = -0.174430
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.396369
obj = -23.747623, rho = -0.134074
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.351513
obj = -27.006658, rho = -0.111761
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.314576
obj = -30.667939, rho = -0.051787
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.279580
obj = -34.812590, rho = -0.058273
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.252761
obj = -39.374742, rho = -0.084644
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.225585
obj = -44.408248, rho = -0.095420
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.197488
obj = -50.075939, rho = -0.118217
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.179385
obj = -56.323110, rho = -0.136927
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.156487
obj = -62.818478, rho = -0.184621
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.139368
obj = -70.346323, rho = -0.083829
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.126309
obj = -77.621812, rho = 0.140984
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -6.509094, rho = 0.153039
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 88% (88/100) (classification)
Accuracy = 87.2% (872/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -7.642223, rho = -0.003880
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.753830
obj = -8.879929, rho = -0.038202
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700709
obj = -10.279697, rho = -0.095882
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.643965
obj = -11.792671, rho = -0.166769
nSV = 67, nBSV = 60
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.577593
obj = -13.526483, rho = -0.124774
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.524160
obj = -15.498900, rho = -0.176390
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.476389
obj = -17.687684, rho = -0.156529
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.427593
obj = -20.128123, rho = -0.143715
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.383377
obj = -22.858654, rho = -0.180624
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.334983
obj = -25.979603, rho = -0.185557
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.303137
obj = -29.527025, rho = -0.098241
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.275264
obj = -33.532770, rho = -0.057240
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.246149
obj = -37.641621, rho = -0.159233
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.222047
obj = -41.981058, rho = -0.152814
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.190033
obj = -46.710633, rho = -0.181345
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.166286
obj = -52.158061, rho = -0.211939
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.149214
obj = -58.231486, rho = -0.219758
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.131643
obj = -64.039385, rho = -0.355531
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.112312
obj = -70.540195, rho = -0.362971
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -6.639909, rho = 0.297808
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 92% (92/100) (classification)
Accuracy = 86.3% (863/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.846881
obj = -7.780192, rho = 0.167537
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.796404
obj = -8.945281, rho = 0.077812
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.733577
obj = -10.157084, rho = 0.068136
nSV = 76, nBSV = 70
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.659702
obj = -11.458525, rho = 0.055314
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.583765
obj = -12.844539, rho = 0.070917
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.515551
obj = -14.361758, rho = 0.045055
nSV = 56, nBSV = 47
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.450800
obj = -16.110126, rho = 0.085882
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.393707
obj = -18.121780, rho = 0.084052
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.344031
obj = -20.447241, rho = 0.111205
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.306436
obj = -23.147475, rho = 0.081002
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.267774
obj = -26.289747, rho = 0.082266
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.240000
obj = -29.892637, rho = 0.174836
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.210886
obj = -34.066025, rho = 0.178074
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.192344
obj = -38.945472, rho = 0.281178
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.174015
obj = -44.152723, rho = 0.348246
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.151350
obj = -50.119451, rho = 0.380333
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*
optimization finished, #iter = 342
nu = 0.131820
obj = -57.442142, rho = 0.371254
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.120000
obj = -66.307221, rho = 0.402872
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.109345
obj = -75.872251, rho = 0.487584
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.887045
obj = -6.582700, rho = 0.086749
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.850053
obj = -7.669128, rho = 0.002549
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780859
obj = -8.812341, rho = 0.020563
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.714663
obj = -10.025440, rho = -0.023875
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.644221
obj = -11.358093, rho = -0.096739
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.565589
obj = -12.837914, rho = -0.115080
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.498915
obj = -14.574414, rho = -0.088151
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.448971
obj = -16.563460, rho = -0.067688
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.398692
obj = -18.805676, rho = -0.099692
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.356996
obj = -21.318950, rho = -0.053916
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.315770
obj = -24.188229, rho = -0.044912
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.283251
obj = -27.486335, rho = 0.001523
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.254765
obj = -31.103776, rho = 0.010768
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.230693
obj = -34.947893, rho = -0.154802
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.199360
obj = -39.203148, rho = -0.218677
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.179620
obj = -43.980241, rho = -0.297735
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.155000
obj = -49.164115, rho = -0.292968
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.134754
obj = -55.416419, rho = -0.278962
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.123544
obj = -62.305508, rho = -0.222929
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.111103
obj = -68.992519, rho = -0.121393
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -7.382398, rho = -0.239556
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -8.696978, rho = -0.240043
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.868594
obj = -10.134003, rho = -0.189485
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.803238
obj = -11.710964, rho = -0.164870
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.729486
obj = -13.461832, rho = -0.140438
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.675682
obj = -15.382863, rho = -0.117318
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.603254
obj = -17.430592, rho = -0.098366
nSV = 65, nBSV = 57
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.534083
obj = -19.785050, rho = -0.142847
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.478922
obj = -22.477928, rho = -0.176811
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.424911
obj = -25.442218, rho = -0.136197
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.375651
obj = -28.908469, rho = -0.124308
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.339927
obj = -32.858927, rho = -0.146987
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.302727
obj = -37.058875, rho = -0.120605
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.276430
obj = -41.539874, rho = -0.235969
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.238915
obj = -46.301557, rho = -0.225468
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.207421
obj = -52.000168, rho = -0.240614
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.189184
obj = -58.069275, rho = -0.342581
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.166647
obj = -64.297954, rho = -0.354844
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.146946
obj = -70.376025, rho = -0.429502
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.122564
obj = -77.333052, rho = -0.433428
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.922732, rho = -0.252366
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.078857, rho = -0.175162
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.837661
obj = -9.264000, rho = -0.121090
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.755929
obj = -10.502617, rho = -0.066037
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.674555
obj = -11.868008, rho = -0.034741
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.595488
obj = -13.370917, rho = -0.043197
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.540000
obj = -15.070433, rho = -0.032019
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.479971
obj = -16.852715, rho = 0.022165
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.418775
obj = -18.805582, rho = 0.045126
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.363669
obj = -21.028406, rho = 0.046448
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.316834
obj = -23.600374, rho = 0.032979
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.286985
obj = -26.427263, rho = 0.092563
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.259013
obj = -29.219561, rho = 0.029014
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.224429
obj = -31.824399, rho = -0.024720
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.192132
obj = -34.666398, rho = -0.004247
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.168418
obj = -37.550026, rho = 0.015632
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.143923
obj = -40.326738, rho = 0.017730
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.122452
obj = -43.180856, rho = 0.036616
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.104192
obj = -45.597034, rho = 0.026364
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.085347
obj = -48.018500, rho = 0.022938
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.959043
obj = -7.340596, rho = -0.340270
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.901771
obj = -8.659520, rho = -0.279433
nSV = 93, nBSV = 89
Total nSV = 93
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.852638
obj = -10.122047, rho = -0.181677
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.798698
obj = -11.735171, rho = -0.127802
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.729106
obj = -13.524634, rho = -0.150067
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.658914
obj = -15.568891, rho = -0.137177
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.592579
obj = -17.960378, rho = -0.102848
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.549073
obj = -20.566617, rho = -0.089983
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.485841
obj = -23.527337, rho = -0.132195
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.451849
obj = -26.843252, rho = -0.088335
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.399847
obj = -30.328910, rho = -0.128192
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.361157
obj = -34.142667, rho = -0.210475
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.313198
obj = -38.438622, rho = -0.228346
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.284771
obj = -43.185823, rho = -0.333133
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.253736
obj = -48.129322, rho = -0.386609
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.218721
obj = -53.436649, rho = -0.398706
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.186846
obj = -59.816826, rho = -0.409624
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.162976
obj = -67.488690, rho = -0.368686
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.142716
obj = -76.701775, rho = -0.444547
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.128723
obj = -87.352430, rho = -0.487390
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -6.804486, rho = -0.606487
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 71% (71/100) (classification)
Accuracy = 70.1% (701/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -8.183051, rho = -0.498557
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 88% (88/100) (classification)
Accuracy = 88.8% (888/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -9.682166, rho = -0.397119
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.769269
obj = -11.253032, rho = -0.279309
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.702711
obj = -12.980472, rho = -0.228130
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.643222
obj = -14.871680, rho = -0.210088
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.585456
obj = -16.932653, rho = -0.200346
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.534155
obj = -19.075893, rho = -0.178260
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.480868
obj = -21.314941, rho = -0.129412
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.417595
obj = -23.691415, rho = -0.119057
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.366542
obj = -26.285852, rho = -0.095199
nSV = 42, nBSV = 32
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.319825
obj = -29.126924, rho = -0.173117
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.285802
obj = -32.097763, rho = -0.285934
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 183
nu = 0.244923
obj = -34.937462, rho = -0.251427
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 214
nu = 0.205678
obj = -38.300651, rho = -0.268245
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.178087
obj = -42.345724, rho = -0.319860
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.154200
obj = -46.769531, rho = -0.205362
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.134630
obj = -51.403447, rho = -0.081020
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.121344
obj = -55.705607, rho = -0.111688
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.101450
obj = -59.694655, rho = -0.117481
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.951134, rho = 0.318961
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 77% (77/100) (classification)
Accuracy = 77% (770/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.281374, rho = 0.132170
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 93% (93/100) (classification)
Accuracy = 91.4% (914/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.830781
obj = -9.638285, rho = 0.008108
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.767923
obj = -11.094475, rho = 0.020876
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700000
obj = -12.710459, rho = 0.032322
nSV = 70, nBSV = 70
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.638042
obj = -14.463348, rho = 0.044915
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.569934
obj = -16.412959, rho = 0.057608
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.506000
obj = -18.602204, rho = 0.020572
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.455041
obj = -20.960677, rho = 0.004673
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.401640
obj = -23.660599, rho = -0.082003
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.370733
obj = -26.418171, rho = -0.103089
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.316853
obj = -29.343900, rho = -0.133016
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.278488
obj = -32.704313, rho = -0.167620
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.241083
obj = -36.432721, rho = -0.184102
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.211494
obj = -40.679198, rho = -0.187778
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.184787
obj = -45.394509, rho = -0.196887
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.161978
obj = -50.690381, rho = -0.167068
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 113
nu = 0.142432
obj = -56.535944, rho = -0.172941
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.123116
obj = -63.107994, rho = -0.173430
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.106414
obj = -70.896813, rho = -0.190725
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.895353
obj = -6.676861, rho = -0.226812
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.836483
obj = -7.819601, rho = -0.181904
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -9.046069, rho = -0.159132
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.733012
obj = -10.327740, rho = -0.141466
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.672770
obj = -11.653343, rho = -0.132736
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.594963
obj = -13.064177, rho = -0.083785
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.520102
obj = -14.664802, rho = -0.065937
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.460874
obj = -16.416233, rho = -0.029970
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.408736
obj = -18.379235, rho = -0.077900
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.359037
obj = -20.511126, rho = -0.101264
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.312216
obj = -22.907335, rho = -0.093023
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.277941
obj = -25.524276, rho = -0.099025
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.239506
obj = -28.412377, rho = -0.094970
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.211937
obj = -31.655188, rho = -0.087948
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.184166
obj = -35.109288, rho = -0.051903
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.158984
obj = -39.240076, rho = -0.027899
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.139118
obj = -44.036868, rho = 0.019521
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.120848
obj = -49.360089, rho = 0.032933
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.104082
obj = -55.966008, rho = -0.001247
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.097392
obj = -63.232984, rho = 0.086392
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -7.432602, rho = -0.025782
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.921344
obj = -8.762940, rho = 0.067156
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.856035
obj = -10.269642, rho = 0.129463
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.805348
obj = -11.959868, rho = 0.171029
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.728950
obj = -13.828998, rho = 0.121836
nSV = 77, nBSV = 71
Total nSV = 77
Accuracy = 95% (95/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.667062
obj = -15.991499, rho = 0.095955
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 95% (95/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.602010
obj = -18.531462, rho = 0.089422
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 95% (95/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.553185
obj = -21.361804, rho = 0.043774
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 96% (96/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.503640
obj = -24.662915, rho = 0.033243
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.462978
obj = -28.297659, rho = -0.016916
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.414291
obj = -32.386826, rho = -0.012951
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 95% (95/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.371344
obj = -37.165530, rho = -0.012979
nSV = 41, nBSV = 32
Total nSV = 41
Accuracy = 95% (95/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.328356
obj = -42.768008, rho = -0.046986
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 94% (94/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.290839
obj = -49.571922, rho = -0.032974
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 94% (94/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.261578
obj = -58.013270, rho = -0.039290
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 95% (95/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.235807
obj = -68.276711, rho = -0.017790
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 95% (95/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.214366
obj = -81.034018, rho = -0.031626
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 94% (94/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.196858
obj = -96.790554, rho = -0.050184
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 94% (94/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.182014
obj = -116.148980, rho = -0.098640
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 94% (94/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.170389
obj = -140.479264, rho = -0.074179
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -7.562130, rho = 0.243817
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 85% (85/100) (classification)
Accuracy = 85.7% (857/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.948547
obj = -8.925946, rho = 0.075670
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.884889
obj = -10.405706, rho = 0.024777
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.821905
obj = -12.056404, rho = 0.000008
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.740000
obj = -13.910273, rho = -0.008614
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.680588
obj = -16.023295, rho = -0.057405
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.634528
obj = -18.267520, rho = 0.065617
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.555158
obj = -20.748368, rho = 0.118730
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.508496
obj = -23.490243, rho = 0.109300
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.454140
obj = -26.399508, rho = 0.115517
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.407053
obj = -29.480744, rho = 0.069715
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.363540
obj = -32.662762, rho = -0.006215
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.311280
obj = -36.054884, rho = -0.015106
nSV = 36, nBSV = 26
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 195
nu = 0.265291
obj = -40.010640, rho = -0.037603
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.....*
optimization finished, #iter = 510
nu = 0.230047
obj = -44.646444, rho = -0.028215
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.209040
obj = -49.760053, rho = -0.122882
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 295
nu = 0.180139
obj = -54.841550, rho = -0.123324
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.153674
obj = -60.745233, rho = -0.099671
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 256
nu = 0.132782
obj = -67.705846, rho = -0.125251
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.113616
obj = -76.188552, rho = -0.129460
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -6.690472, rho = 0.436659
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 76% (76/100) (classification)
Accuracy = 65.8% (658/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.820000
obj = -7.997918, rho = 0.282148
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 89% (89/100) (classification)
Accuracy = 82.3% (823/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.800000
obj = -9.369843, rho = 0.141820
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 94% (94/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.745206
obj = -10.821944, rho = 0.086881
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.680000
obj = -12.430781, rho = 0.062141
nSV = 68, nBSV = 68
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.615099
obj = -14.190562, rho = 0.041889
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.566218
obj = -16.084893, rho = 0.055699
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.501858
obj = -18.102761, rho = 0.068678
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.440910
obj = -20.382246, rho = 0.062660
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.391216
obj = -22.980684, rho = 0.070707
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.339404
obj = -26.004322, rho = 0.077339
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.301163
obj = -29.660800, rho = 0.137799
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.272402
obj = -33.698905, rho = 0.190578
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.241411
obj = -38.146755, rho = 0.157098
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.211421
obj = -43.429354, rho = 0.173983
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.196773
obj = -49.166158, rho = 0.237376
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.178489
obj = -54.778616, rho = 0.216100
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.160839
obj = -60.448596, rho = 0.256868
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.137066
obj = -65.817719, rho = 0.269605
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 279
nu = 0.116053
obj = -72.241452, rho = 0.266920
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.892141
obj = -6.628462, rho = -0.370959
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 95% (95/100) (classification)
Accuracy = 92.2% (922/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.844642
obj = -7.713296, rho = -0.293801
nSV = 86, nBSV = 81
Total nSV = 86
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.768674
obj = -8.925230, rho = -0.259496
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.708311
obj = -10.288310, rho = -0.217715
nSV = 75, nBSV = 69
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.647343
obj = -11.787000, rho = -0.187481
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.584154
obj = -13.475913, rho = -0.157362
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.531320
obj = -15.278112, rho = -0.096485
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.465601
obj = -17.335244, rho = -0.090033
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.414701
obj = -19.755723, rho = -0.086629
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.372051
obj = -22.544250, rho = -0.130278
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.331759
obj = -25.711934, rho = -0.147051
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.304487
obj = -29.247572, rho = -0.227155
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.267409
obj = -33.001252, rho = -0.251658
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.239240
obj = -37.325641, rho = -0.298381
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.214896
obj = -42.029312, rho = -0.283260
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.192540
obj = -47.106776, rho = -0.229873
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.167097
obj = -52.601764, rho = -0.225323
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.148135
obj = -58.869990, rho = -0.222661
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.131862
obj = -65.631691, rho = -0.136970
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.117100
obj = -72.360738, rho = 0.008878
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -7.071588, rho = -0.385187
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.888785
obj = -8.249001, rho = -0.349989
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.831030
obj = -9.547450, rho = -0.286934
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.755028
obj = -10.996721, rho = -0.315595
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.683124
obj = -12.645650, rho = -0.269130
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.620899
obj = -14.529997, rho = -0.263516
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.565734
obj = -16.604081, rho = -0.240807
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.495308
obj = -19.020070, rho = -0.259523
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.451062
obj = -21.821058, rho = -0.274043
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.401983
obj = -25.101715, rho = -0.291525
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.368810
obj = -28.755693, rho = -0.260668
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.335376
obj = -32.895421, rho = -0.254126
nSV = 34, nBSV = 31
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.301106
obj = -37.269728, rho = -0.219557
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.272313
obj = -42.049022, rho = -0.181624
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.238679
obj = -47.462024, rho = -0.128074
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.207681
obj = -53.824814, rho = -0.123242
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.185346
obj = -61.264158, rho = -0.063452
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.168736
obj = -69.508626, rho = -0.028325
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.155634
obj = -78.191474, rho = -0.112130
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.137171
obj = -86.442798, rho = -0.146392
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -7.344891, rho = -0.068104
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 91% (91/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920319
obj = -8.608304, rho = -0.151104
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 94% (94/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -9.987778, rho = -0.133231
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 94% (94/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.787479
obj = -11.541738, rho = -0.127408
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.705360
obj = -13.348040, rho = -0.128641
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 95% (95/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.638728
obj = -15.437655, rho = -0.097562
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 95% (95/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.588578
obj = -17.856887, rho = -0.066633
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.540000
obj = -20.624195, rho = -0.089677
nSV = 54, nBSV = 54
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.491624
obj = -23.556354, rho = -0.187926
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.434294
obj = -26.978215, rho = -0.176242
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.389622
obj = -31.052821, rho = -0.248014
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.351859
obj = -35.676553, rho = -0.337185
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.316258
obj = -41.057467, rho = -0.350356
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.284182
obj = -47.345888, rho = -0.350237
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.268161
obj = -54.247829, rho = -0.447845
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.242280
obj = -61.343043, rho = -0.567551
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.214584
obj = -69.134456, rho = -0.678563
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.189918
obj = -78.038684, rho = -0.706988
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.167738
obj = -88.153503, rho = -0.748705
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.149480
obj = -99.839771, rho = -0.806792
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.450578, rho = -0.395899
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 93% (930/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.832944
obj = -7.514654, rho = -0.302766
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.757698
obj = -8.655762, rho = -0.278078
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.684191
obj = -9.949263, rho = -0.286936
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.640000
obj = -11.374146, rho = -0.270589
nSV = 65, nBSV = 63
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.582646
obj = -12.821343, rho = -0.258865
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.512487
obj = -14.374739, rho = -0.281570
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.453978
obj = -16.107054, rho = -0.288267
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.404354
obj = -17.953264, rho = -0.369157
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.347457
obj = -20.015618, rho = -0.355130
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.304978
obj = -22.370947, rho = -0.353856
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.267827
obj = -25.037993, rho = -0.411466
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.234128
obj = -28.032097, rho = -0.403156
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.209765
obj = -31.325969, rho = -0.413195
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.186102
obj = -34.764363, rho = -0.408130
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.167330
obj = -37.829189, rho = -0.480299
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.149872
obj = -40.492182, rho = -0.637257
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.124512
obj = -42.520799, rho = -0.672812
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.**.*
optimization finished, #iter = 156
nu = 0.102024
obj = -44.627603, rho = -0.694145
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.083572
obj = -47.004058, rho = -0.696473
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.878085, rho = -0.099274
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 92% (92/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.869553
obj = -8.012951, rho = -0.156784
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.811849
obj = -9.255420, rho = -0.158047
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.740000
obj = -10.611742, rho = -0.153081
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.661324
obj = -12.160390, rho = -0.147508
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.596320
obj = -13.928522, rho = -0.165859
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.545082
obj = -15.958768, rho = -0.217980
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.483048
obj = -18.233336, rho = -0.207373
nSV = 50, nBSV = 47
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.436361
obj = -20.789791, rho = -0.184656
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.386085
obj = -23.755481, rho = -0.183809
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.347568
obj = -27.182845, rho = -0.174081
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.316361
obj = -30.960524, rho = -0.095654
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.279444
obj = -35.302054, rho = -0.096230
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.248758
obj = -40.361424, rho = -0.071323
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.226768
obj = -46.075303, rho = -0.075446
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.207398
obj = -52.218028, rho = -0.012569
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.184053
obj = -58.620408, rho = 0.027528
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.163139
obj = -65.908233, rho = 0.022242
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.141375
obj = -74.184766, rho = 0.048195
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.127870
obj = -83.714440, rho = 0.057044
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.749159, rho = -0.015827
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 91.3% (913/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.836816
obj = -7.921362, rho = -0.142754
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.783879
obj = -9.262423, rho = -0.118696
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.741030
obj = -10.691083, rho = -0.089663
nSV = 77, nBSV = 71
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.665517
obj = -12.270107, rho = -0.125469
nSV = 70, nBSV = 64
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.611958
obj = -14.014171, rho = -0.110916
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.548566
obj = -15.926338, rho = -0.148383
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.494530
obj = -18.080725, rho = -0.104294
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.446171
obj = -20.371540, rho = -0.077691
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.387115
obj = -22.991245, rho = -0.091489
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.348173
obj = -25.895490, rho = -0.090131
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.305761
obj = -29.114674, rho = -0.051881
nSV = 37, nBSV = 27
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.269986
obj = -32.855113, rho = -0.037187
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.239368
obj = -37.100600, rho = 0.023320
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.217326
obj = -41.569409, rho = 0.028093
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.187794
obj = -46.350323, rho = 0.039111
nSV = 26, nBSV = 15
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.165449
obj = -51.722609, rho = 0.025316
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.144198
obj = -57.776438, rho = 0.057194
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.124353
obj = -64.884589, rho = 0.066953
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.114764
obj = -72.831302, rho = 0.069698
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.820000
obj = -6.583110, rho = 0.250596
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 81% (81/100) (classification)
Accuracy = 76.8% (768/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -7.837877, rho = 0.133563
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 89% (89/100) (classification)
Accuracy = 86.5% (865/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.778890
obj = -9.185397, rho = 0.061074
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 94% (94/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.722526
obj = -10.657242, rho = 0.006527
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.666361
obj = -12.257146, rho = -0.017940
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.605820
obj = -14.052397, rho = 0.029013
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.537350
obj = -16.111851, rho = 0.053778
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.495736
obj = -18.396067, rho = 0.099132
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.444684
obj = -20.856551, rho = 0.116700
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.403393
obj = -23.587627, rho = 0.107124
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.354146
obj = -26.534670, rho = 0.076551
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.311513
obj = -29.892820, rho = 0.065484
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.275496
obj = -33.776742, rho = 0.059001
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.247070
obj = -38.080615, rho = 0.017434
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.222831
obj = -42.712211, rho = 0.147215
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.192442
obj = -47.842029, rho = 0.154324
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.175625
obj = -53.456333, rho = 0.178637
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.154574
obj = -58.714288, rho = 0.139222
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.134245
obj = -64.408527, rho = 0.156791
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.118929
obj = -69.627868, rho = 0.337075
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.925995
obj = -6.962074, rho = -0.236124
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.863782
obj = -8.166957, rho = -0.226681
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 94% (94/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.812693
obj = -9.535017, rho = -0.186341
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.750821
obj = -11.030385, rho = -0.112363
nSV = 78, nBSV = 71
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.680801
obj = -12.724120, rho = -0.131804
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.621079
obj = -14.659495, rho = -0.102859
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.558994
obj = -16.894504, rho = -0.116273
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.515564
obj = -19.433594, rho = -0.107166
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.459625
obj = -22.232159, rho = -0.122980
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.410789
obj = -25.470837, rho = -0.123477
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.370875
obj = -29.189332, rho = -0.071451
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.333383
obj = -33.466365, rho = -0.071837
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.302642
obj = -38.282191, rho = -0.086291
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.272811
obj = -43.642853, rho = -0.150932
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.247804
obj = -49.463248, rho = -0.192338
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.217869
obj = -55.986507, rho = -0.152944
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.197014
obj = -63.300669, rho = -0.207911
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 116
nu = 0.174920
obj = -71.264517, rho = -0.217190
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.153949
obj = -80.032497, rho = -0.229918
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 298
nu = 0.132746
obj = -90.549359, rho = -0.231387
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.902805
obj = -6.670851, rho = -0.247222
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.847806
obj = -7.741433, rho = -0.188194
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.798395
obj = -8.903430, rho = -0.114157
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.730261
obj = -10.109658, rho = -0.087337
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.641668
obj = -11.445925, rho = -0.035705
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.575538
obj = -12.946292, rho = 0.021193
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.522095
obj = -14.545660, rho = 0.023019
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.462582
obj = -16.211656, rho = 0.024508
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.405285
obj = -18.033537, rho = 0.007086
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.350660
obj = -20.104526, rho = -0.017772
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.312757
obj = -22.404319, rho = -0.076286
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.267363
obj = -24.946447, rho = -0.026307
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.234359
obj = -27.889298, rho = -0.068590
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.209404
obj = -31.160639, rho = -0.059709
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.181750
obj = -34.552186, rho = -0.082209
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 193
nu = 0.156377
obj = -38.559461, rho = -0.050347
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.135465
obj = -43.278593, rho = -0.071650
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.121035
obj = -48.586210, rho = -0.070757
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.107045
obj = -54.327497, rho = -0.037715
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.094051
obj = -60.612579, rho = -0.042644
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.956615
obj = -7.116738, rho = -0.059543
nSV = 97, nBSV = 94
Total nSV = 97
Accuracy = 96% (96/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.885707
obj = -8.334168, rho = -0.014544
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 96% (96/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -9.713907, rho = -0.104719
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -11.157839, rho = -0.101703
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.712484
obj = -12.708986, rho = -0.083862
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.636304
obj = -14.375774, rho = -0.072395
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.571127
obj = -16.247529, rho = -0.030458
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.509059
obj = -18.219989, rho = -0.019006
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.442682
obj = -20.474043, rho = 0.004031
nSV = 48, nBSV = 39
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.385681
obj = -23.203797, rho = 0.004293
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.346068
obj = -26.346701, rho = 0.040546
nSV = 40, nBSV = 30
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.306099
obj = -29.985183, rho = 0.045721
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.275529
obj = -33.954757, rho = 0.001828
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.239815
obj = -38.650375, rho = 0.004189
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*...*
optimization finished, #iter = 414
nu = 0.216529
obj = -44.032823, rho = -0.040699
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.192498
obj = -50.163997, rho = -0.019383
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.171156
obj = -57.371456, rho = -0.005771
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.153088
obj = -65.850308, rho = 0.068150
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.139463
obj = -75.610098, rho = 0.076844
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.128811
obj = -85.793661, rho = -0.035415
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.840000
obj = -6.466729, rho = -0.452042
nSV = 87, nBSV = 82
Total nSV = 87
Accuracy = 94% (94/100) (classification)
Accuracy = 85% (850/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -7.574619, rho = -0.334349
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.773037
obj = -8.724673, rho = -0.277900
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.708980
obj = -9.944979, rho = -0.228086
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.641069
obj = -11.231667, rho = -0.156755
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.574272
obj = -12.631102, rho = -0.181114
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.507823
obj = -14.148164, rho = -0.254972
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.441053
obj = -15.881947, rho = -0.223506
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.383343
obj = -17.881690, rho = -0.223404
nSV = 43, nBSV = 34
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.343358
obj = -20.217642, rho = -0.213689
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.303631
obj = -22.708025, rho = -0.197197
nSV = 37, nBSV = 26
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.271794
obj = -25.569085, rho = -0.193089
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.240000
obj = -28.506742, rho = -0.246836
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.214762
obj = -31.626436, rho = -0.330473
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.185412
obj = -35.009454, rho = -0.282175
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.160074
obj = -38.780621, rho = -0.268903
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.138340
obj = -43.242112, rho = -0.261017
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.123051
obj = -48.083290, rho = -0.187605
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.104920
obj = -53.491592, rho = -0.205068
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.091980
obj = -59.958613, rho = -0.280168
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.913846
obj = -6.921486, rho = 0.024342
nSV = 93, nBSV = 89
Total nSV = 93
Accuracy = 94% (94/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.868895
obj = -8.116949, rho = -0.071163
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.817224
obj = -9.432741, rho = -0.108164
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.746249
obj = -10.866977, rho = -0.076509
nSV = 78, nBSV = 72
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.684422
obj = -12.448895, rho = -0.015437
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.613496
obj = -14.201916, rho = -0.008702
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.555749
obj = -16.188659, rho = -0.052706
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.508072
obj = -18.333624, rho = -0.046519
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.450871
obj = -20.628619, rho = -0.026014
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.401312
obj = -23.132788, rho = 0.000775
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.345417
obj = -25.990844, rho = 0.000324
nSV = 40, nBSV = 31
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.305636
obj = -29.432025, rho = 0.040725
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.273912
obj = -33.270185, rho = 0.121026
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.241937
obj = -37.432251, rho = 0.122656
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.218561
obj = -42.015492, rho = 0.076032
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.199963
obj = -46.719896, rho = 0.139511
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.171396
obj = -51.184396, rho = 0.144023
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.151807
obj = -55.921088, rho = 0.020080
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.128128
obj = -60.514929, rho = -0.048383
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.109945
obj = -65.442157, rho = -0.131472
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.740000
obj = -6.338286, rho = 0.560216
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 63% (63/100) (classification)
Accuracy = 54.2% (542/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -7.705648, rho = 0.439594
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 76% (76/100) (classification)
Accuracy = 67.7% (677/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.740000
obj = -9.216578, rho = 0.285889
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 92% (92/100) (classification)
Accuracy = 85.2% (852/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.733762
obj = -10.767696, rho = 0.120694
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.673671
obj = -12.382681, rho = 0.112516
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.617277
obj = -14.168571, rho = 0.138931
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.547786
obj = -16.151599, rho = 0.121619
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.498105
obj = -18.433194, rho = 0.090593
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.445940
obj = -20.902827, rho = 0.022108
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.399768
obj = -23.640218, rho = -0.033300
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.353018
obj = -26.730516, rho = 0.023906
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.312680
obj = -30.229713, rho = 0.049508
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.280604
obj = -34.225222, rho = 0.043268
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.249733
obj = -38.521779, rho = 0.056001
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.214550
obj = -43.615186, rho = 0.045487
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.195346
obj = -49.467191, rho = 0.139964
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.177266
obj = -55.610334, rho = 0.114611
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.155341
obj = -62.289544, rho = 0.172830
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.133655
obj = -69.919286, rho = 0.218094
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.120083
obj = -79.088967, rho = 0.147031
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.027949, rho = -0.492572
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 77% (77/100) (classification)
Accuracy = 70% (700/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -8.406106, rho = -0.353397
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 89% (89/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.840000
obj = -9.847411, rho = -0.291176
nSV = 84, nBSV = 84
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.796634
obj = -11.350836, rho = -0.206119
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.717448
obj = -12.969887, rho = -0.239384
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.650696
obj = -14.783359, rho = -0.191314
nSV = 66, nBSV = 64
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.579317
obj = -16.754576, rho = -0.180153
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.515079
obj = -18.974038, rho = -0.236758
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.456107
obj = -21.554086, rho = -0.176871
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.405700
obj = -24.476077, rho = -0.229862
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.362093
obj = -27.820130, rho = -0.206257
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.321563
obj = -31.655178, rho = -0.237870
nSV = 36, nBSV = 26
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.281506
obj = -36.307403, rho = -0.227667
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.260311
obj = -41.366304, rho = -0.130711
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.237585
obj = -46.962230, rho = -0.103909
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.206251
obj = -53.158141, rho = -0.139699
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.181453
obj = -60.428497, rho = -0.196824
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 169
nu = 0.160012
obj = -69.177307, rho = -0.217290
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.143611
obj = -79.859762, rho = -0.238432
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.131748
obj = -91.999678, rho = -0.265349
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.876900
obj = -6.488007, rho = -0.052503
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.807255
obj = -7.588650, rho = -0.017612
nSV = 84, nBSV = 79
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.757493
obj = -8.858528, rho = -0.108955
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.709745
obj = -10.231264, rho = -0.133008
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.642672
obj = -11.713563, rho = -0.105469
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.590566
obj = -13.321678, rho = -0.048572
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.522209
obj = -15.065816, rho = -0.048154
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.461090
obj = -17.079288, rho = -0.004714
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.419015
obj = -19.398739, rho = -0.012589
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.371239
obj = -21.852697, rho = 0.001489
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.325840
obj = -24.667294, rho = 0.022167
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.292729
obj = -27.778546, rho = 0.061004
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.256182
obj = -31.277237, rho = 0.064879
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.228242
obj = -35.308980, rho = 0.091693
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.209243
obj = -39.608721, rho = 0.073899
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.187474
obj = -43.558466, rho = 0.078253
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.160414
obj = -47.546685, rho = 0.093262
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.137731
obj = -51.970418, rho = 0.134438
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.120102
obj = -56.494488, rho = 0.184436
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.103150
obj = -61.243226, rho = 0.243278
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.920000
obj = -7.155980, rho = 0.206782
nSV = 95, nBSV = 90
Total nSV = 95
Accuracy = 91% (91/100) (classification)
Accuracy = 92.2% (922/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.895128
obj = -8.420038, rho = 0.059797
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.843805
obj = -9.805854, rho = 0.072653
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -11.309425, rho = 0.046541
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.705441
obj = -12.956285, rho = 0.017394
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.640000
obj = -14.833120, rho = -0.009141
nSV = 65, nBSV = 63
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.572166
obj = -16.914049, rho = -0.034098
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.520930
obj = -19.231032, rho = 0.013797
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.459469
obj = -21.828583, rho = 0.032153
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.414984
obj = -24.856382, rho = -0.003050
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.371788
obj = -28.185385, rho = 0.008742
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.327169
obj = -32.014892, rho = 0.046998
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.290814
obj = -36.539428, rho = 0.101674
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.262872
obj = -41.587230, rho = 0.158239
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.237830
obj = -47.021063, rho = 0.226683
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.209769
obj = -53.125618, rho = 0.281413
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.182505
obj = -60.330375, rho = 0.340714
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.162330
obj = -68.726014, rho = 0.400326
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.146914
obj = -78.402691, rho = 0.468185
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.130377
obj = -89.167377, rho = 0.509713
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.914230
obj = -6.851446, rho = -0.192483
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.851392
obj = -8.039114, rho = -0.151136
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.802340
obj = -9.357927, rho = -0.096793
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740195
obj = -10.806171, rho = -0.041906
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.669040
obj = -12.442796, rho = -0.011712
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.619904
obj = -14.311935, rho = -0.001770
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.559306
obj = -16.270385, rho = -0.031520
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.497848
obj = -18.525699, rho = -0.051721
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.445190
obj = -21.075481, rho = -0.099791
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.403362
obj = -23.819102, rho = -0.084809
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.352548
obj = -26.966165, rho = -0.070150
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.315971
obj = -30.544016, rho = -0.055394
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.286809
obj = -34.306582, rho = -0.016843
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.255386
obj = -38.352314, rho = 0.007970
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.221079
obj = -42.820808, rho = -0.006014
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.195535
obj = -47.824069, rho = 0.012928
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 296
nu = 0.171902
obj = -53.136091, rho = -0.016225
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 290
nu = 0.145787
obj = -59.456657, rho = -0.022496
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.125625
obj = -67.372431, rho = -0.044049
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.109551
obj = -77.130007, rho = -0.040664
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -7.450771, rho = 0.227209
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 84% (84/100) (classification)
Accuracy = 83.6% (836/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.902663
obj = -8.889389, rho = 0.079204
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 88% (88/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.874808
obj = -10.455265, rho = 0.001731
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.816627
obj = -12.165076, rho = -0.054581
nSV = 84, nBSV = 79
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.753145
obj = -14.056638, rho = -0.030463
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.687778
obj = -16.175282, rho = -0.048677
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.612634
obj = -18.633536, rho = -0.051433
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.549688
obj = -21.564932, rho = -0.024293
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.499441
obj = -24.976204, rho = -0.042075
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.447786
obj = -29.050493, rho = -0.030503
nSV = 50, nBSV = 42
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.406776
obj = -33.948074, rho = -0.013901
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.376297
obj = -39.671353, rho = 0.006403
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.352165
obj = -46.079473, rho = 0.121759
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.322264
obj = -53.294687, rho = 0.137554
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.290196
obj = -61.585576, rho = 0.114643
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.266079
obj = -70.938411, rho = 0.127465
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.238573
obj = -81.767825, rho = 0.151945
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.211704
obj = -94.833713, rho = 0.156463
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.190023
obj = -110.682844, rho = 0.141061
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.170614
obj = -130.349127, rho = 0.159735
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.892360
obj = -6.603390, rho = -0.144031
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.839750
obj = -7.703182, rho = -0.186787
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.777025
obj = -8.906299, rho = -0.176109
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.713361
obj = -10.228375, rho = -0.128668
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.649785
obj = -11.671412, rho = -0.139404
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.584646
obj = -13.257559, rho = -0.186168
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.518367
obj = -15.053215, rho = -0.231444
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.465996
obj = -17.077650, rho = -0.167517
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.414551
obj = -19.313469, rho = -0.096055
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.365462
obj = -21.896123, rho = -0.079849
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.334503
obj = -24.737175, rho = 0.016753
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.297793
obj = -27.608408, rho = -0.010741
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.256431
obj = -30.908083, rho = 0.004316
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.223273
obj = -34.777358, rho = 0.023171
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.198507
obj = -39.247791, rho = 0.139042
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.179826
obj = -44.117138, rho = 0.160215
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.155335
obj = -49.472961, rho = 0.122924
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.136939
obj = -55.734007, rho = 0.117622
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.125177
obj = -62.207151, rho = 0.066806
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.112557
obj = -68.346759, rho = 0.026199
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.864038
obj = -6.386088, rho = -0.146806
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 93% (93/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.802620
obj = -7.433826, rho = -0.204422
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.740000
obj = -8.632009, rho = -0.225353
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.677699
obj = -9.985098, rho = -0.228809
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.629347
obj = -11.470124, rho = -0.201651
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.569573
obj = -13.112758, rho = -0.171647
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.510722
obj = -14.923415, rho = -0.233126
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.453943
obj = -17.018143, rho = -0.259051
nSV = 47, nBSV = 44
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.408114
obj = -19.393089, rho = -0.289073
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.374477
obj = -21.952939, rho = -0.216067
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.331153
obj = -24.693200, rho = -0.204029
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.293300
obj = -27.731068, rho = -0.253247
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.259336
obj = -31.058649, rho = -0.248606
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.227542
obj = -34.846786, rho = -0.299992
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.195489
obj = -39.301918, rho = -0.330306
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.176946
obj = -44.413233, rho = -0.271398
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.153956
obj = -50.185801, rho = -0.212198
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.139361
obj = -56.789211, rho = -0.292099
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.120827
obj = -64.102811, rho = -0.339550
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.104078
obj = -73.237725, rho = -0.340297
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.955280
obj = -7.231750, rho = -0.113490
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.901434
obj = -8.487596, rho = -0.042949
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.858069
obj = -9.849646, rho = -0.053770
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.783297
obj = -11.309806, rho = -0.084276
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.719486
obj = -12.909978, rho = -0.080892
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.648109
obj = -14.658424, rho = -0.047170
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.568966
obj = -16.651845, rho = -0.037767
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.517939
obj = -18.916253, rho = -0.115051
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.472033
obj = -21.180591, rho = -0.106078
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.410741
obj = -23.617590, rho = -0.097008
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.358973
obj = -26.409249, rho = -0.083973
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.314289
obj = -29.538023, rho = -0.081071
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.279166
obj = -33.098182, rho = -0.050045
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.243459
obj = -37.007377, rho = -0.094447
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.212857
obj = -41.381031, rho = -0.157042
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.182398
obj = -46.611956, rho = -0.178594
nSV = 27, nBSV = 15
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.167248
obj = -52.608975, rho = -0.067085
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.152019
obj = -58.213584, rho = -0.060022
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.135349
obj = -63.748957, rho = -0.079761
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.117834
obj = -68.610140, rho = -0.040285
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -6.547573, rho = -0.331227
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.847010
obj = -7.569646, rho = -0.353135
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.780729
obj = -8.665898, rho = -0.309132
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.695173
obj = -9.853004, rho = -0.283141
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.634826
obj = -11.173536, rho = -0.250071
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.567827
obj = -12.587338, rho = -0.210103
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.509499
obj = -14.034793, rho = -0.159904
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.440391
obj = -15.660953, rho = -0.168701
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.387814
obj = -17.555971, rho = -0.145195
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.335322
obj = -19.672496, rho = -0.153967
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.298886
obj = -22.153456, rho = -0.153117
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.261174
obj = -24.877528, rho = -0.136880
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.229564
obj = -28.086160, rho = -0.152857
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.201837
obj = -31.719645, rho = -0.146809
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.174593
obj = -36.160636, rho = -0.146744
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 224
nu = 0.152967
obj = -41.655877, rho = -0.159596
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.140000
obj = -48.384703, rho = -0.127814
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.133504
obj = -55.377376, rho = 0.018717
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.121027
obj = -62.263044, rho = 0.101686
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.104324
obj = -70.137443, rho = 0.102200
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -6.490637, rho = -0.630333
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 70% (70/100) (classification)
Accuracy = 66.4% (664/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -7.813231, rho = -0.528943
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 88% (88/100) (classification)
Accuracy = 84% (840/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.760000
obj = -9.252081, rho = -0.453405
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 95% (95/100) (classification)
Accuracy = 92.5% (925/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -10.738553, rho = -0.363022
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.680000
obj = -12.297967, rho = -0.303146
nSV = 69, nBSV = 67
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.620000
obj = -13.986157, rho = -0.290267
nSV = 63, nBSV = 60
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.560950
obj = -15.737177, rho = -0.290124
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.504767
obj = -17.583850, rho = -0.333013
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.437991
obj = -19.531368, rho = -0.292207
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.389490
obj = -21.678326, rho = -0.286757
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.336868
obj = -23.924241, rho = -0.264520
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.289935
obj = -26.440778, rho = -0.266166
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.256964
obj = -29.298470, rho = -0.209496
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.
WARNING: using -h 0 may be faster
*
optimization finished, #iter = 172
nu = 0.219839
obj = -32.325350, rho = -0.219294
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.188346
obj = -35.828481, rho = -0.228351
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.167161
obj = -39.539473, rho = -0.254484
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.144859
obj = -43.662722, rho = -0.197658
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.124099
obj = -48.248102, rho = -0.159998
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.109102
obj = -53.231635, rho = -0.108819
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.095443
obj = -58.104960, rho = -0.031954
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.928456
obj = -6.924255, rho = -0.072286
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.865760
obj = -8.119751, rho = -0.088842
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.821988
obj = -9.422425, rho = -0.136159
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.742724
obj = -10.840872, rho = -0.150267
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.686153
obj = -12.426436, rho = -0.114305
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.618312
obj = -14.177854, rho = -0.065656
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.559955
obj = -16.141322, rho = -0.086916
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.500745
obj = -18.301397, rho = -0.078873
nSV = 52, nBSV = 43
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.434561
obj = -20.802111, rho = -0.070582
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.386900
obj = -23.801823, rho = -0.122664
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.352302
obj = -27.153299, rho = -0.140837
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.306901
obj = -31.062083, rho = -0.136563
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.271615
obj = -35.878932, rho = -0.121845
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.250430
obj = -41.605421, rho = -0.177151
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.231533
obj = -47.720711, rho = -0.306100
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.208090
obj = -54.731853, rho = -0.360552
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.190866
obj = -62.223339, rho = -0.415698
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.175248
obj = -69.992222, rho = -0.318532
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.155830
obj = -77.915710, rho = -0.277727
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.134864
obj = -86.716569, rho = -0.303336
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -6.993214, rho = -0.112320
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.872913
obj = -8.234463, rho = -0.173408
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.826937
obj = -9.593886, rho = -0.196320
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.760000
obj = -11.078879, rho = -0.187491
nSV = 77, nBSV = 75
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.698404
obj = -12.749163, rho = -0.146854
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.622585
obj = -14.605133, rho = -0.116755
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.575355
obj = -16.655074, rho = -0.053652
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.515627
obj = -18.781125, rho = -0.005348
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.467028
obj = -21.150079, rho = 0.024496
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.411678
obj = -23.647553, rho = 0.002167
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.362202
obj = -26.479854, rho = 0.005510
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.321117
obj = -29.471844, rho = 0.038736
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.289835
obj = -32.455449, rho = -0.015373
nSV = 30, nBSV = 27
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.246267
obj = -35.434281, rho = -0.020664
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.209837
obj = -38.861636, rho = -0.019699
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.182041
obj = -42.819875, rho = -0.062304
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.155730
obj = -46.972763, rho = -0.087269
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.134578
obj = -51.828258, rho = -0.121867
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.118975
obj = -56.691330, rho = -0.201234
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.099352
obj = -62.102519, rho = -0.186775
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.898911
obj = -6.730382, rho = -0.328330
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.838627
obj = -7.883286, rho = -0.297561
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.780406
obj = -9.196900, rho = -0.209067
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.722437
obj = -10.649825, rho = -0.180571
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.671338
obj = -12.248382, rho = -0.191148
nSV = 68, nBSV = 66
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.602273
obj = -14.003406, rho = -0.189308
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.542308
obj = -15.985377, rho = -0.202532
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.483458
obj = -18.268813, rho = -0.224876
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.440000
obj = -20.870351, rho = -0.141754
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.393338
obj = -23.652621, rho = -0.148393
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.347200
obj = -26.930915, rho = -0.154183
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.319934
obj = -30.475976, rho = -0.254256
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.277085
obj = -34.424311, rho = -0.229143
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.249449
obj = -39.129743, rho = -0.244922
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.225339
obj = -43.957702, rho = -0.228176
nSV = 28, nBSV = 17
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.199137
obj = -49.342200, rho = -0.199742
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.175441
obj = -55.287208, rho = -0.209780
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 281
nu = 0.152448
obj = -62.081188, rho = -0.203900
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*...*
optimization finished, #iter = 510
nu = 0.132403
obj = -69.942377, rho = -0.211539
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.118022
obj = -79.204602, rho = -0.226470
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -6.561414, rho = -0.341091
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.868025
obj = -7.495010, rho = -0.255459
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.792269
obj = -8.460769, rho = -0.202740
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.703645
obj = -9.497851, rho = -0.197965
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.620000
obj = -10.653863, rho = -0.149192
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.550836
obj = -11.923497, rho = -0.146420
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.482943
obj = -13.319923, rho = -0.112270
nSV = 50, nBSV = 48
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.423527
obj = -14.780514, rho = -0.106148
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.369728
obj = -16.460699, rho = -0.111380
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.318015
obj = -18.408252, rho = -0.093043
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.288761
obj = -20.495071, rho = -0.065417
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.253402
obj = -22.514398, rho = -0.042857
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.218457
obj = -24.690218, rho = -0.043001
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.185987
obj = -27.154635, rho = -0.083669
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.157794
obj = -30.068457, rho = -0.095984
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.138656
obj = -33.415572, rho = -0.132676
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.128007
obj = -36.478992, rho = -0.128767
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.109195
obj = -39.060836, rho = -0.126110
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.091966
obj = -41.596009, rho = -0.124655
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.075507
obj = -44.568155, rho = -0.101157
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.857592
obj = -6.489342, rho = -0.463804
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 92% (92/100) (classification)
Accuracy = 89.5% (895/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.807899
obj = -7.612757, rho = -0.416654
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 92% (92/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.761560
obj = -8.841749, rho = -0.359209
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 94% (94/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.691129
obj = -10.228346, rho = -0.343077
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 94% (94/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.621954
obj = -11.835607, rho = -0.307939
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.575886
obj = -13.696957, rho = -0.256156
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.520000
obj = -15.772652, rho = -0.263843
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.466896
obj = -18.191779, rho = -0.215718
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.423606
obj = -21.095061, rho = -0.257824
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.391570
obj = -24.253671, rho = -0.330008
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.355460
obj = -27.764320, rho = -0.405682
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.320838
obj = -31.657436, rho = -0.370789
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.283400
obj = -36.256401, rho = -0.361976
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.265704
obj = -41.254653, rho = -0.252639
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.233911
obj = -46.399576, rho = -0.247545
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.206687
obj = -52.529114, rho = -0.287979
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.182265
obj = -59.488472, rho = -0.319359
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*...*
optimization finished, #iter = 300
nu = 0.167912
obj = -67.057064, rho = -0.358193
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.151844
obj = -74.148393, rho = -0.449885
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 170
nu = 0.129474
obj = -81.829769, rho = -0.456624
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -6.629874, rho = -0.515860
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 77% (77/100) (classification)
Accuracy = 76.6% (766/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -7.899521, rho = -0.383072
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 94% (94/100) (classification)
Accuracy = 92% (920/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -9.254967, rho = -0.328814
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.720000
obj = -10.749043, rho = -0.283179
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.660000
obj = -12.454532, rho = -0.229575
nSV = 67, nBSV = 65
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.603833
obj = -14.345338, rho = -0.177200
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.553948
obj = -16.475505, rho = -0.147158
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.495569
obj = -18.875297, rho = -0.157995
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.452949
obj = -21.545752, rho = -0.172935
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.410911
obj = -24.472642, rho = -0.223228
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.375367
obj = -27.548970, rho = -0.317232
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.332344
obj = -30.763389, rho = -0.377241
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.288351
obj = -34.253008, rho = -0.375060
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 99.5% (995/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.250955
obj = -38.405388, rho = -0.351659
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.220920
obj = -42.955105, rho = -0.437719
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.191712
obj = -48.286897, rho = -0.457875
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.168010
obj = -54.671011, rho = -0.462149
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.150145
obj = -61.932522, rho = -0.454278
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.136020
obj = -69.826620, rho = -0.438306
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.124348
obj = -77.716018, rho = -0.436057
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.914592
obj = -6.731600, rho = -0.300355
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.858552
obj = -7.825786, rho = -0.235713
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.792590
obj = -9.016196, rho = -0.170867
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.731817
obj = -10.289183, rho = -0.113177
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.653085
obj = -11.684077, rho = -0.094351
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.596880
obj = -13.195228, rho = -0.015156
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.536093
obj = -14.776931, rho = 0.010131
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.467602
obj = -16.467187, rho = 0.002069
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.410089
obj = -18.356975, rho = 0.042490
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.355323
obj = -20.458355, rho = 0.082752
nSV = 41, nBSV = 32
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.315472
obj = -22.885818, rho = 0.104221
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.283155
obj = -25.398880, rho = 0.142229
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.247107
obj = -27.893521, rho = 0.144905
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.218466
obj = -30.441436, rho = 0.150921
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.189140
obj = -32.807985, rho = 0.180087
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.158105
obj = -35.186219, rho = 0.193338
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.131019
obj = -38.002122, rho = 0.211229
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.114248
obj = -41.093397, rho = 0.282550
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.099711
obj = -43.549184, rho = 0.267368
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.084668
obj = -45.436003, rho = 0.267294
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.696315, rho = 0.097370
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.859375
obj = -7.809021, rho = -0.005559
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.803259
obj = -8.959398, rho = -0.044776
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.725057
obj = -10.176770, rho = 0.003612
nSV = 77, nBSV = 71
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.650103
obj = -11.549911, rho = -0.005243
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.587049
obj = -13.053356, rho = 0.048149
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.528240
obj = -14.659339, rho = 0.075980
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.467512
obj = -16.345677, rho = 0.110177
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.413610
obj = -18.119911, rho = 0.180231
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.356185
obj = -20.097848, rho = 0.190550
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.305459
obj = -22.390960, rho = 0.191629
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.269948
obj = -25.027938, rho = 0.181826
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.237964
obj = -27.861103, rho = 0.138878
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.210018
obj = -30.747733, rho = 0.250429
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.178293
obj = -34.033417, rho = 0.267611
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.155617
obj = -37.931352, rho = 0.265853
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.136792
obj = -42.245639, rho = 0.215369
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.120561
obj = -46.761266, rho = 0.134926
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.102930
obj = -51.883047, rho = 0.106699
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.089579
obj = -57.754024, rho = 0.211694
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.860000
obj = -6.750968, rho = -0.469212
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 83% (83/100) (classification)
Accuracy = 81.3% (813/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.847303
obj = -7.959193, rho = -0.342142
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.800000
obj = -9.281655, rho = -0.359216
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.745597
obj = -10.679131, rho = -0.304449
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.680421
obj = -12.151318, rho = -0.281273
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.617997
obj = -13.697580, rho = -0.235373
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.547175
obj = -15.372714, rho = -0.217612
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.489270
obj = -17.209922, rho = -0.207163
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.425120
obj = -19.214321, rho = -0.179212
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.377528
obj = -21.502771, rho = -0.144405
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.327627
obj = -24.012430, rho = -0.121252
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.290919
obj = -26.730006, rho = -0.142045
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.257505
obj = -29.740584, rho = -0.112733
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.222921
obj = -32.832167, rho = -0.094510
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.195763
obj = -36.326649, rho = -0.076435
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.169647
obj = -39.964704, rho = -0.072250
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.152336
obj = -43.460548, rho = -0.068163
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.129361
obj = -46.734350, rho = -0.143711
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.108063
obj = -50.264917, rho = -0.128217
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.095363
obj = -53.879248, rho = -0.241518
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.929218
obj = -6.746262, rho = -0.321152
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -7.773007, rho = -0.254871
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.808031
obj = -8.860504, rho = -0.254880
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.731088
obj = -10.022479, rho = -0.217761
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.659570
obj = -11.209340, rho = -0.134899
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.576599
obj = -12.516852, rho = -0.154465
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.510937
obj = -13.974902, rho = -0.141186
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.445940
obj = -15.531685, rho = -0.126503
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.382734
obj = -17.315363, rho = -0.121204
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.347312
obj = -19.222279, rho = -0.073631
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.296971
obj = -21.188925, rho = -0.050486
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.263166
obj = -23.316879, rho = -0.117454
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.224222
obj = -25.580149, rho = -0.054058
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.194318
obj = -28.204401, rho = -0.047211
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.169894
obj = -30.976822, rho = -0.054656
nSV = 19, nBSV = 15
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.150102
obj = -33.673839, rho = 0.029977
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.127007
obj = -36.305393, rho = 0.009112
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.109215
obj = -38.981179, rho = -0.026846
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.093664
obj = -41.556672, rho = -0.049482
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 249
nu = 0.078308
obj = -43.717146, rho = -0.019636
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.962014
obj = -7.030356, rho = -0.198007
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -8.150996, rho = -0.134894
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.831729
obj = -9.374132, rho = -0.156607
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.754346
obj = -10.695178, rho = -0.166987
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.688428
obj = -12.118547, rho = -0.201585
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.616587
obj = -13.658905, rho = -0.229621
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.540194
obj = -15.384776, rho = -0.230491
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.474945
obj = -17.362463, rho = -0.207449
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.419585
obj = -19.647366, rho = -0.209002
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.378869
obj = -22.201730, rho = -0.278571
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.338672
obj = -24.946484, rho = -0.263484
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.295588
obj = -27.935011, rho = -0.246887
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.256531
obj = -31.519442, rho = -0.254705
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.225975
obj = -35.696493, rho = -0.228902
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.206966
obj = -40.186937, rho = -0.127372
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.185193
obj = -44.911748, rho = -0.159486
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.162186
obj = -49.887747, rho = -0.215843
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.141679
obj = -55.589259, rho = -0.239960
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.124180
obj = -61.632816, rho = -0.247668
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.107079
obj = -68.337934, rho = -0.273179
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.065698, rho = -0.346404
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 94% (94/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.875923
obj = -8.302013, rho = -0.291138
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.814793
obj = -9.706993, rho = -0.281717
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.769196
obj = -11.286072, rho = -0.315157
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.687072
obj = -13.025184, rho = -0.334438
nSV = 73, nBSV = 67
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.630647
obj = -15.061571, rho = -0.287274
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.577217
obj = -17.404192, rho = -0.254730
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.522340
obj = -19.993840, rho = -0.224358
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.478915
obj = -22.899813, rho = -0.196064
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.432354
obj = -26.148692, rho = -0.214199
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.388653
obj = -29.824649, rho = -0.249325
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.347267
obj = -33.848639, rho = -0.245656
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.309662
obj = -38.299461, rho = -0.206285
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.273824
obj = -43.397660, rho = -0.185013
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.252017
obj = -49.124881, rho = -0.161949
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.221907
obj = -54.934833, rho = -0.174641
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.196429
obj = -61.543574, rho = -0.263729
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.173591
obj = -68.512690, rho = -0.335826
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.156495
obj = -75.840643, rho = -0.414793
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.136816
obj = -82.602060, rho = -0.495029
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.926930
obj = -6.986044, rho = -0.252897
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.880000
obj = -8.170782, rho = -0.216581
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -9.480546, rho = -0.180172
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.760744
obj = -10.871724, rho = -0.174494
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.687842
obj = -12.419590, rho = -0.232411
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.615671
obj = -14.164181, rho = -0.286567
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.558947
obj = -16.077485, rho = -0.223255
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.492822
obj = -18.242407, rho = -0.202221
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.433184
obj = -20.802997, rho = -0.187005
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.385798
obj = -23.820798, rho = -0.254182
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.343773
obj = -27.326787, rho = -0.320113
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.310944
obj = -31.525501, rho = -0.283122
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.279344
obj = -36.285815, rho = -0.287642
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.247672
obj = -42.059716, rho = -0.316876
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.229833
obj = -48.877021, rho = -0.475765
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.209242
obj = -56.522259, rho = -0.528861
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.189501
obj = -65.164338, rho = -0.621144
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.169686
obj = -75.383417, rho = -0.733785
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.150917
obj = -87.988371, rho = -0.713429
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.140736
obj = -102.895385, rho = -0.606571
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -7.015030, rho = -0.421069
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 92% (92/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.879327
obj = -8.256944, rho = -0.318315
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.816486
obj = -9.612396, rho = -0.282651
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.771030
obj = -11.108390, rho = -0.204827
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.707904
obj = -12.648953, rho = -0.143063
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.633222
obj = -14.359101, rho = -0.125147
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.565537
obj = -16.235626, rho = -0.117236
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.495916
obj = -18.389427, rho = -0.137831
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.440196
obj = -20.965096, rho = -0.095108
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.392739
obj = -23.889985, rho = -0.069154
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.353039
obj = -27.164637, rho = -0.120420
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.315875
obj = -30.881188, rho = -0.183490
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.280730
obj = -35.143426, rho = -0.252483
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.247232
obj = -40.183985, rho = -0.291066
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.221713
obj = -46.002995, rho = -0.372330
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.197637
obj = -52.877991, rho = -0.356739
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.179206
obj = -60.865298, rho = -0.399642
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.162197
obj = -70.041285, rho = -0.321366
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.144286
obj = -80.622650, rho = -0.370463
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.127710
obj = -93.611049, rho = -0.365728
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.922147
obj = -6.626154, rho = -0.108746
nSV = 94, nBSV = 90
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.854956
obj = -7.657296, rho = -0.150817
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.787408
obj = -8.769014, rho = -0.203326
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.712317
obj = -9.982109, rho = -0.141599
nSV = 74, nBSV = 67
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.640000
obj = -11.301052, rho = -0.145635
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.570947
obj = -12.759736, rho = -0.151810
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.510932
obj = -14.389866, rho = -0.119153
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.454184
obj = -16.110100, rho = -0.067851
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.402357
obj = -17.956697, rho = -0.073710
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.348668
obj = -20.035302, rho = -0.057181
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.309896
obj = -22.337799, rho = -0.066275
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.273738
obj = -24.754754, rho = -0.141302
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.236304
obj = -27.257180, rho = -0.153592
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.205791
obj = -30.138015, rho = -0.171610
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.183381
obj = -33.183519, rho = -0.132688
nSV = 20, nBSV = 16
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.155693
obj = -36.180269, rho = -0.063838
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.135536
obj = -39.559981, rho = -0.043294
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 259
nu = 0.117696
obj = -42.712836, rho = -0.024814
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 299
nu = 0.097926
obj = -46.048952, rho = -0.037474
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.083045
obj = -50.064886, rho = -0.022496
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.980000
obj = -7.659808, rho = -0.247275
nSV = 98, nBSV = 98
Total nSV = 98
Accuracy = 94% (94/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.946618
obj = -9.045366, rho = -0.181571
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 94% (94/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.887617
obj = -10.598359, rho = -0.123713
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.828866
obj = -12.329191, rho = -0.127662
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.775860
obj = -14.226528, rho = -0.090184
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.711429
obj = -16.255135, rho = -0.055697
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 96% (96/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.644805
obj = -18.438066, rho = -0.078448
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 96% (96/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.574641
obj = -20.776892, rho = -0.076650
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 96% (96/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.506488
obj = -23.431937, rho = -0.097169
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.459860
obj = -26.377387, rho = -0.076508
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.399196
obj = -29.593778, rho = -0.115769
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 96% (96/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.353565
obj = -33.275473, rho = -0.163414
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.303835
obj = -37.470870, rho = -0.138163
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.266789
obj = -42.502769, rho = -0.081272
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.234652
obj = -48.533519, rho = -0.043703
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.208231
obj = -55.714575, rho = -0.007423
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.195413
obj = -63.788389, rho = 0.030916
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 319
nu = 0.171144
obj = -72.511604, rho = 0.055843
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.150717
obj = -83.074611, rho = 0.080312
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.136144
obj = -95.680302, rho = 0.129506
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.880000
obj = -7.035611, rho = 0.364209
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 83% (83/100) (classification)
Accuracy = 80.1% (801/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.348647, rho = 0.189828
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 95% (95/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.836066
obj = -9.685518, rho = 0.102531
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.780518
obj = -11.131155, rho = 0.045541
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.706335
obj = -12.692531, rho = 0.001575
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.636919
obj = -14.449655, rho = -0.024378
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.568023
obj = -16.358768, rho = -0.018781
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.497907
obj = -18.593507, rho = -0.007365
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.452921
obj = -21.105228, rho = 0.061172
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.399452
obj = -23.894399, rho = 0.013161
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.352757
obj = -27.133281, rho = 0.069810
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.312003
obj = -30.858800, rho = 0.099139
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.275336
obj = -35.316372, rho = 0.049589
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.242470
obj = -40.685362, rho = 0.004526
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.218207
obj = -47.271700, rho = 0.063864
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.199010
obj = -54.935438, rho = 0.195376
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.177646
obj = -64.145776, rho = 0.192739
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.159067
obj = -75.760907, rho = 0.185642
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.147457
obj = -90.107852, rho = 0.123326
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.139816
obj = -107.135331, rho = -0.010774
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -6.361510, rho = -0.653090
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 73% (73/100) (classification)
Accuracy = 62.8% (628/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -7.673459, rho = -0.557942
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 88% (88/100) (classification)
Accuracy = 80.3% (803/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.759541
obj = -9.075240, rho = -0.437326
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 95% (95/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.718675
obj = -10.540442, rho = -0.366090
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 96% (96/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.657589
obj = -12.126606, rho = -0.366945
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.595376
obj = -13.912263, rho = -0.358131
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.540000
obj = -15.952947, rho = -0.437436
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.491892
obj = -18.165057, rho = -0.473582
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.438948
obj = -20.646990, rho = -0.425938
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.391402
obj = -23.390283, rho = -0.415152
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.345422
obj = -26.636165, rho = -0.409125
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.311559
obj = -30.338958, rho = -0.393644
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.275119
obj = -34.408266, rho = -0.365341
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.244422
obj = -39.303204, rho = -0.332894
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.218371
obj = -44.794098, rho = -0.318304
nSV = 24, nBSV = 20
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.194691
obj = -51.255841, rho = -0.357124
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.175042
obj = -58.457922, rho = -0.344857
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.154893
obj = -67.118967, rho = -0.351192
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.142519
obj = -76.871596, rho = -0.201402
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.129501
obj = -87.332578, rho = -0.135671
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.874234
obj = -6.496875, rho = -0.241160
nSV = 90, nBSV = 86
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.826286
obj = -7.580726, rho = -0.224967
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.765334
obj = -8.756740, rho = -0.243456
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.701445
obj = -10.066402, rho = -0.247282
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.631640
obj = -11.508184, rho = -0.220302
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.568185
obj = -13.145762, rho = -0.217484
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.513079
obj = -15.004164, rho = -0.197124
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.460453
obj = -17.082173, rho = -0.216541
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.416423
obj = -19.378890, rho = -0.226322
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.373989
obj = -21.831904, rho = -0.201266
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.333879
obj = -24.515188, rho = -0.209269
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.288251
obj = -27.498910, rho = -0.251261
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.256075
obj = -30.928448, rho = -0.227810
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.222354
obj = -34.912526, rho = -0.245090
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.194644
obj = -39.646143, rho = -0.241181
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.177326
obj = -45.007355, rho = -0.253873
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.155636
obj = -51.033555, rho = -0.304357
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.139584
obj = -57.782816, rho = -0.372927
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.122880
obj = -65.445168, rho = -0.376344
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.108758
obj = -74.503907, rho = -0.405171
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.922144
obj = -6.876114, rho = -0.294846
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 92% (92/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.853429
obj = -8.049383, rho = -0.276087
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 93% (93/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.795222
obj = -9.385688, rho = -0.228668
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 95% (95/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.740000
obj = -10.884047, rho = -0.200184
nSV = 76, nBSV = 72
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.670284
obj = -12.578778, rho = -0.223972
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.603275
obj = -14.553803, rho = -0.250442
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.560128
obj = -16.732403, rho = -0.164728
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.510102
obj = -19.133931, rho = -0.128827
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.470224
obj = -21.727027, rho = -0.106506
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.410828
obj = -24.567202, rho = -0.089742
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.372188
obj = -27.667438, rho = -0.024893
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.327158
obj = -31.102183, rho = -0.002013
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.286888
obj = -34.984781, rho = -0.005518
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.248142
obj = -39.611264, rho = 0.009341
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.222949
obj = -45.085934, rho = -0.043827
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.206762
obj = -50.769619, rho = -0.125628
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.181835
obj = -56.717450, rho = -0.091587
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.160296
obj = -62.979652, rho = -0.062382
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.141502
obj = -69.888632, rho = -0.045713
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.129824
obj = -76.296551, rho = 0.086588
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.903504
obj = -6.891955, rho = -0.279894
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 91% (91/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.860000
obj = -8.106425, rho = -0.237263
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.798034
obj = -9.468485, rho = -0.172966
nSV = 83, nBSV = 77
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.740000
obj = -11.018564, rho = -0.127078
nSV = 74, nBSV = 74
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.688909
obj = -12.749536, rho = -0.050762
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.622362
obj = -14.651741, rho = -0.006925
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.571627
obj = -16.785003, rho = 0.005391
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.512459
obj = -19.158860, rho = 0.058808
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.461680
obj = -21.845353, rho = 0.120117
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.408774
obj = -24.886386, rho = 0.158095
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.365482
obj = -28.372447, rho = 0.182766
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.328101
obj = -32.298067, rho = 0.214843
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.294879
obj = -36.834187, rho = 0.217351
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.274107
obj = -41.606028, rho = 0.114092
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.242111
obj = -46.347198, rho = 0.057607
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.209439
obj = -51.780215, rho = 0.052986
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.184822
obj = -57.962685, rho = 0.066727
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.164684
obj = -64.495394, rho = 0.035470
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.145538
obj = -71.415053, rho = 0.024398
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.126742
obj = -78.460934, rho = -0.006904
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.912797
obj = -6.652726, rho = 0.098290
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 91.8% (918/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.858520
obj = -7.689881, rho = 0.008364
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.790837
obj = -8.808064, rho = -0.002566
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.708121
obj = -10.032944, rho = 0.000350
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.635474
obj = -11.392621, rho = 0.048270
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.577121
obj = -12.934633, rho = 0.064238
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.512976
obj = -14.590857, rho = 0.075576
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.455023
obj = -16.434475, rho = 0.026312
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.408536
obj = -18.447622, rho = 0.067454
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.358660
obj = -20.644846, rho = 0.026708
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.313281
obj = -23.139493, rho = -0.009054
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.277497
obj = -25.932807, rho = 0.003247
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.242569
obj = -29.041119, rho = -0.044050
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.220166
obj = -32.329560, rho = -0.170995
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.188010
obj = -35.895542, rho = -0.186750
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.165792
obj = -39.796398, rho = -0.232352
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.146863
obj = -43.968721, rho = -0.156486
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.129298
obj = -47.928178, rho = -0.024120
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.116645
obj = -51.545582, rho = 0.123879
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.099505
obj = -53.996162, rho = 0.220057
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -7.182731, rho = -0.094370
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.907196
obj = -8.390006, rho = -0.073323
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.840000
obj = -9.720814, rho = -0.123605
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.771964
obj = -11.175820, rho = -0.054644
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.712622
obj = -12.762150, rho = -0.092446
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.640745
obj = -14.501412, rho = -0.106127
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.561164
obj = -16.481252, rho = -0.093820
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.505775
obj = -18.707453, rho = -0.143929
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.459039
obj = -21.220566, rho = -0.140313
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.416326
obj = -23.834701, rho = -0.195299
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.370997
obj = -26.331799, rho = -0.158494
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.319941
obj = -29.121570, rho = -0.160344
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.274771
obj = -32.364060, rho = -0.161844
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.241047
obj = -35.947069, rho = -0.191723
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.210008
obj = -39.961215, rho = -0.201220
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.180070
obj = -44.577750, rho = -0.178967
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.153961
obj = -50.249166, rho = -0.173246
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.137640
obj = -57.054421, rho = -0.193733
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.124871
obj = -64.384615, rho = -0.183193
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.109913
obj = -72.354585, rho = -0.193412
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.660000
obj = -5.919756, rho = -0.790069
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 67% (67/100) (classification)
Accuracy = 48.1% (481/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.660000
obj = -7.305651, rho = -0.732490
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 68% (68/100) (classification)
Accuracy = 49.4% (494/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.660000
obj = -8.923364, rho = -0.658983
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 75% (75/100) (classification)
Accuracy = 61.5% (615/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.660000
obj = -10.743967, rho = -0.565931
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 87% (87/100) (classification)
Accuracy = 83.1% (831/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.649677
obj = -12.678005, rho = -0.463155
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 96% (96/100) (classification)
Accuracy = 93.2% (932/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.627405
obj = -14.691582, rho = -0.365153
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.564996
obj = -16.817213, rho = -0.353729
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.516365
obj = -19.225121, rho = -0.307774
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.460493
obj = -21.898986, rho = -0.309345
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.406955
obj = -25.030334, rho = -0.299645
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.362706
obj = -28.719053, rho = -0.294961
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.330529
obj = -32.833309, rho = -0.390035
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.296206
obj = -37.629513, rho = -0.423927
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.266159
obj = -43.019593, rho = -0.448602
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.243854
obj = -49.046315, rho = -0.432000
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.217616
obj = -55.686449, rho = -0.366759
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.193637
obj = -63.039725, rho = -0.335712
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.171382
obj = -71.656801, rho = -0.320961
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.155848
obj = -80.984287, rho = -0.251966
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.141921
obj = -90.658743, rho = -0.167682
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.940000
obj = -7.322624, rho = 0.201879
nSV = 95, nBSV = 93
Total nSV = 95
Accuracy = 87% (87/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920000
obj = -8.620431, rho = 0.043731
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 95% (95/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -10.019142, rho = -0.007547
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.800000
obj = -11.527652, rho = -0.002855
nSV = 81, nBSV = 79
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.719034
obj = -13.197940, rho = -0.006206
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.647054
obj = -15.139487, rho = 0.017614
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.587580
obj = -17.336043, rho = 0.036342
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.530989
obj = -19.786214, rho = 0.060746
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.480000
obj = -22.579807, rho = 0.020412
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.431867
obj = -25.476120, rho = -0.044068
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.382705
obj = -28.789602, rho = -0.109249
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.340681
obj = -32.465419, rho = -0.158021
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.302026
obj = -36.483510, rho = -0.147867
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.277340
obj = -40.723777, rho = -0.148236
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.241615
obj = -44.695637, rho = -0.116691
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.206615
obj = -49.274116, rho = -0.098223
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.182981
obj = -54.142682, rho = -0.109380
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 322
nu = 0.154369
obj = -59.408876, rho = -0.113308
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*......*
optimization finished, #iter = 841
nu = 0.131676
obj = -65.667817, rho = -0.089201
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.113611
obj = -73.147257, rho = -0.082093
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.920000
obj = -6.839711, rho = -0.226195
nSV = 93, nBSV = 90
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.858571
obj = -8.012482, rho = -0.268743
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.800000
obj = -9.320254, rho = -0.261258
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.727063
obj = -10.800777, rho = -0.260062
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.680000
obj = -12.427781, rho = -0.190312
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.614890
obj = -14.188795, rho = -0.149586
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.546830
obj = -16.166547, rho = -0.134734
nSV = 59, nBSV = 51
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.491527
obj = -18.529034, rho = -0.173341
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.438536
obj = -21.235524, rho = -0.217764
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.395609
obj = -24.319686, rho = -0.245916
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.363575
obj = -27.774371, rho = -0.154541
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.323400
obj = -31.408082, rho = -0.127572
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.288589
obj = -35.548732, rho = -0.152332
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.258540
obj = -40.242479, rho = -0.219091
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.230731
obj = -45.263134, rho = -0.315947
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.206111
obj = -51.008586, rho = -0.390292
nSV = 24, nBSV = 20
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.187657
obj = -56.522260, rho = -0.396020
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.164577
obj = -61.851461, rho = -0.462477
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.141662
obj = -67.524725, rho = -0.473908
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.123869
obj = -72.913099, rho = -0.486612
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.677373, rho = -0.324091
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 91% (91/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.831994
obj = -7.839556, rho = -0.250341
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 94% (94/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.780000
obj = -9.130866, rho = -0.265558
nSV = 78, nBSV = 78
Total nSV = 78
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.720000
obj = -10.553918, rho = -0.203135
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.655860
obj = -12.154206, rho = -0.198010
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.591117
obj = -13.998174, rho = -0.186713
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.527084
obj = -16.152310, rho = -0.174332
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.483351
obj = -18.618005, rho = -0.123931
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.431659
obj = -21.481189, rho = -0.173536
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.401720
obj = -24.773363, rho = -0.095986
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.362272
obj = -28.351766, rho = -0.131239
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.322765
obj = -32.557176, rho = -0.064544
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.293141
obj = -37.400115, rho = -0.045793
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.263236
obj = -42.914188, rho = -0.173943
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.242139
obj = -49.073939, rho = -0.084344
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.226477
obj = -55.319703, rho = -0.035287
nSV = 24, nBSV = 20
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.199464
obj = -61.424265, rho = -0.157437
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.180000
obj = -68.283082, rho = -0.009150
nSV = 20, nBSV = 16
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.168208
obj = -72.818773, rho = -0.013789
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.141837
obj = -75.961057, rho = -0.014121
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.901606
obj = -6.703350, rho = 0.016211
nSV = 92, nBSV = 89
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.847164
obj = -7.804339, rho = -0.031981
nSV = 87, nBSV = 83
Total nSV = 87
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.793352
obj = -9.019180, rho = -0.091277
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.722466
obj = -10.321892, rho = -0.028866
nSV = 77, nBSV = 69
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.650558
obj = -11.804869, rho = 0.016644
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.590217
obj = -13.460747, rho = 0.069991
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.524650
obj = -15.293365, rho = 0.073447
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.467685
obj = -17.390112, rho = 0.058764
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.429542
obj = -19.731041, rho = -0.013570
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.376199
obj = -22.276150, rho = -0.007068
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.330676
obj = -25.168076, rho = -0.006339
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.293616
obj = -28.530667, rho = -0.024801
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.262772
obj = -32.372926, rho = 0.075689
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.229816
obj = -36.813956, rho = 0.110695
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.200697
obj = -42.160540, rho = 0.111644
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.184990
obj = -48.495709, rho = 0.048147
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.167565
obj = -55.218920, rho = 0.037244
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.149471
obj = -62.922308, rho = 0.145196
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.132642
obj = -71.542247, rho = 0.246951
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.117051
obj = -82.008728, rho = 0.223262
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.880000
obj = -6.973468, rho = -0.478063
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 85% (85/100) (classification)
Accuracy = 83.4% (834/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.879491
obj = -8.247744, rho = -0.335172
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.824925
obj = -9.595705, rho = -0.266414
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.766850
obj = -11.058573, rho = -0.202621
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.704878
obj = -12.626473, rho = -0.194441
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.641642
obj = -14.278938, rho = -0.203019
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.569540
obj = -16.010418, rho = -0.165197
nSV = 60, nBSV = 52
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.506086
obj = -17.976184, rho = -0.153037
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.442050
obj = -20.154313, rho = -0.147459
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.387859
obj = -22.591649, rho = -0.187313
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.341008
obj = -25.381892, rho = -0.174579
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.297807
obj = -28.602666, rho = -0.145116
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.266431
obj = -32.280036, rho = -0.151455
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.244494
obj = -36.078653, rho = -0.143941
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.213723
obj = -39.825608, rho = -0.166729
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.182948
obj = -44.133835, rho = -0.140903
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.159188
obj = -48.917010, rho = -0.228449
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.136983
obj = -54.321496, rho = -0.277715
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.116040
obj = -60.974278, rho = -0.279277
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.105168
obj = -68.980312, rho = -0.331590
nSV = 13, nBSV = 9
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.890644, rho = -0.400006
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 91% (91/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.856960
obj = -8.066784, rho = -0.425113
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 92% (92/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.810870
obj = -9.397681, rho = -0.380769
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.756889
obj = -10.789445, rho = -0.328464
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.681237
obj = -12.306925, rho = -0.336678
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.617994
obj = -13.957910, rho = -0.272415
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.549438
obj = -15.816577, rho = -0.245034
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.487148
obj = -17.897106, rho = -0.181347
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.431606
obj = -20.292533, rho = -0.209700
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.386277
obj = -23.058598, rho = -0.192114
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.340689
obj = -26.107440, rho = -0.157404
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.300689
obj = -29.746745, rho = -0.116377
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.264902
obj = -34.102284, rho = -0.104065
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.240892
obj = -39.257241, rho = -0.068885
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.220339
obj = -44.843865, rho = -0.014906
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.194309
obj = -51.224315, rho = -0.017139
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.179100
obj = -58.457624, rho = -0.022307
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.158895
obj = -66.091067, rho = 0.014112
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.138492
obj = -75.412882, rho = -0.011860
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.132902
obj = -85.071778, rho = 0.023816
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.375641, rho = -0.328899
nSV = 89, nBSV = 87
Total nSV = 89
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.813661
obj = -7.385514, rho = -0.301254
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.760000
obj = -8.492610, rho = -0.229548
nSV = 76, nBSV = 76
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.691459
obj = -9.660230, rho = -0.206722
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.624832
obj = -10.912343, rho = -0.246557
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.559345
obj = -12.249852, rho = -0.288506
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.492486
obj = -13.685397, rho = -0.258457
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.442551
obj = -15.272706, rho = -0.296461
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 24
nu = 0.382099
obj = -16.973228, rho = -0.253031
nSV = 40, nBSV = 37
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.331874
obj = -18.820005, rho = -0.205302
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.300491
obj = -20.732649, rho = -0.222573
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.255688
obj = -22.664293, rho = -0.217745
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.223573
obj = -24.761765, rho = -0.172941
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.196099
obj = -26.713931, rho = -0.257294
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.166529
obj = -28.522557, rho = -0.259457
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.138095
obj = -30.466514, rho = -0.240762
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.117839
obj = -32.492718, rho = -0.141959
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.096797
obj = -34.637317, rho = -0.138929
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.079692
obj = -37.239074, rho = -0.130345
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.067058
obj = -40.351242, rho = -0.156216
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.800000
obj = -6.511289, rho = -0.533172
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 76% (76/100) (classification)
Accuracy = 72.9% (729/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.800000
obj = -7.776866, rho = -0.405133
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 92% (92/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.780000
obj = -9.095623, rho = -0.275906
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.740000
obj = -10.447900, rho = -0.176197
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.667818
obj = -11.875662, rho = -0.146835
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.600000
obj = -13.445688, rho = -0.143875
nSV = 60, nBSV = 60
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.537529
obj = -15.129289, rho = -0.127985
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.472744
obj = -17.024462, rho = -0.110293
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.419836
obj = -19.106776, rho = -0.133918
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.369049
obj = -21.401724, rho = -0.179761
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.324171
obj = -24.023437, rho = -0.188621
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.289718
obj = -26.918525, rho = -0.160918
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.256798
obj = -29.834851, rho = -0.030573
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.232980
obj = -32.911671, rho = -0.079407
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.199791
obj = -35.596967, rho = -0.058481
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.170944
obj = -38.584124, rho = -0.054366
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.142882
obj = -41.828173, rho = -0.082908
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.120677
obj = -45.710020, rho = -0.078254
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.104504
obj = -50.132017, rho = -0.118593
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.091621
obj = -54.454521, rho = -0.180486
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.763899, rho = -0.494514
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 92% (92/100) (classification)
Accuracy = 90.3% (903/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.842955
obj = -7.909101, rho = -0.423082
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 92.7% (927/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.791089
obj = -9.219882, rho = -0.403979
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 95% (95/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.736956
obj = -10.650388, rho = -0.353880
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 95% (95/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.668651
obj = -12.168571, rho = -0.357825
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.609419
obj = -13.847369, rho = -0.340563
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 94% (94/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.544777
obj = -15.699700, rho = -0.312522
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 95% (95/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.482349
obj = -17.807939, rho = -0.314682
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.430910
obj = -20.175546, rho = -0.314731
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.380769
obj = -22.864245, rho = -0.380175
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.335301
obj = -26.048233, rho = -0.375138
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.294426
obj = -29.834223, rho = -0.330041
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.264343
obj = -34.396756, rho = -0.307618
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.237703
obj = -39.731899, rho = -0.335851
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.214464
obj = -46.014553, rho = -0.357495
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.194151
obj = -53.465509, rho = -0.344283
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.175850
obj = -62.163641, rho = -0.396024
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.157793
obj = -72.758397, rho = -0.421417
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.144975
obj = -85.682164, rho = -0.459014
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.140263
obj = -99.996173, rho = -0.538028
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -6.580554, rho = -0.436143
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 87% (87/100) (classification)
Accuracy = 81.3% (813/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.822538
obj = -7.756336, rho = -0.322080
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 92.1% (921/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.781023
obj = -9.005214, rho = -0.239250
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.720000
obj = -10.379447, rho = -0.224599
nSV = 72, nBSV = 72
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.650266
obj = -11.875586, rho = -0.265658
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.582688
obj = -13.594832, rho = -0.263266
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.529513
obj = -15.499985, rho = -0.235948
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.481190
obj = -17.577571, rho = -0.194163
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.423583
obj = -19.900062, rho = -0.160427
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.380000
obj = -22.566016, rho = -0.118511
nSV = 40, nBSV = 37
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.348101
obj = -25.377213, rho = -0.172141
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.311920
obj = -28.249710, rho = -0.280065
nSV = 32, nBSV = 29
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.271494
obj = -31.207627, rho = -0.282048
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.229657
obj = -34.588504, rho = -0.255350
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.205413
obj = -38.506842, rho = -0.279487
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.175586
obj = -42.706558, rho = -0.239758
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.149678
obj = -47.820331, rho = -0.238068
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.133985
obj = -53.679031, rho = -0.315025
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.117311
obj = -60.058991, rho = -0.323371
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.102892
obj = -67.026481, rho = -0.294330
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.920553
obj = -6.754463, rho = -0.240731
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.875035
obj = -7.807580, rho = -0.210636
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.796802
obj = -8.969222, rho = -0.146813
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.738008
obj = -10.212502, rho = -0.186267
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.651840
obj = -11.560080, rho = -0.162684
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.578528
obj = -13.087223, rho = -0.125362
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.523402
obj = -14.731579, rho = -0.111413
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.461670
obj = -16.562278, rho = -0.150036
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.406113
obj = -18.600169, rho = -0.174167
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.362544
obj = -20.853238, rho = -0.154806
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.323049
obj = -23.304350, rho = -0.118213
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.284867
obj = -25.824796, rho = -0.118331
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.250380
obj = -28.491366, rho = -0.133023
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.213762
obj = -31.296530, rho = -0.116290
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.183118
obj = -34.655053, rho = -0.099146
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.161883
obj = -38.183469, rho = -0.118768
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.139157
obj = -42.085511, rho = -0.145010
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.122073
obj = -46.289684, rho = -0.202138
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.102967
obj = -50.881076, rho = -0.199372
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.090252
obj = -56.218346, rho = -0.115380
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.960000
obj = -7.148439, rho = -0.271416
nSV = 97, nBSV = 95
Total nSV = 97
Accuracy = 97% (97/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.895615
obj = -8.354932, rho = -0.293978
nSV = 91, nBSV = 86
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.841169
obj = -9.702857, rho = -0.186937
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.789894
obj = -11.062989, rho = -0.100492
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.715565
obj = -12.570983, rho = -0.070233
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.629609
obj = -14.224879, rho = -0.047641
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.558627
obj = -16.116491, rho = -0.051512
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.502897
obj = -18.227902, rho = -0.069557
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.447389
obj = -20.541804, rho = -0.063964
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.396850
obj = -23.065823, rho = -0.092304
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.345381
obj = -25.954816, rho = -0.117386
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.304773
obj = -29.364307, rho = -0.152422
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.273045
obj = -33.160798, rho = -0.216941
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.241315
obj = -37.355802, rho = -0.229807
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.211340
obj = -42.173188, rho = -0.206620
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.193283
obj = -47.395553, rho = -0.207138
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 229
nu = 0.173687
obj = -52.532959, rho = -0.154003
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.151205
obj = -57.896971, rho = -0.281513
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.131044
obj = -63.815537, rho = -0.289807
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.114017
obj = -70.130931, rho = -0.241665
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.903146
obj = -6.629903, rho = -0.199055
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.841586
obj = -7.697406, rho = -0.155831
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.783734
obj = -8.857956, rho = -0.092697
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.699833
obj = -10.155521, rho = -0.080504
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.637603
obj = -11.655851, rho = -0.021335
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.577778
obj = -13.282740, rho = -0.002891
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.526251
obj = -15.106454, rho = -0.058073
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.482135
obj = -16.974740, rho = -0.057458
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.429078
obj = -18.907943, rho = 0.000898
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.372374
obj = -20.952813, rho = -0.048004
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.322769
obj = -23.217987, rho = -0.064785
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.280000
obj = -25.853650, rho = -0.043084
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.244047
obj = -28.692168, rho = 0.054921
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.208736
obj = -32.138021, rho = 0.098138
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.181841
obj = -36.174063, rho = 0.050933
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.164613
obj = -40.804570, rho = -0.048897
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.149798
obj = -45.307179, rho = -0.144254
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.130077
obj = -49.876378, rho = -0.184771
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.110513
obj = -55.159626, rho = -0.212567
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.097134
obj = -61.074859, rho = -0.187474
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.852326
obj = -6.289726, rho = -0.214365
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.798030
obj = -7.330421, rho = -0.209029
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.725117
obj = -8.511340, rho = -0.195035
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.664499
obj = -9.869068, rho = -0.239688
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.613446
obj = -11.430606, rho = -0.263569
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.570284
obj = -13.108440, rho = -0.285686
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.522199
obj = -14.849170, rho = -0.230747
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.469819
obj = -16.690846, rho = -0.212703
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.413450
obj = -18.651128, rho = -0.190750
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.358879
obj = -20.916970, rho = -0.168982
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.317644
obj = -23.543236, rho = -0.150922
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.279277
obj = -26.400142, rho = -0.136020
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.247833
obj = -29.633706, rho = -0.147049
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.223508
obj = -32.895014, rho = -0.210565
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.198620
obj = -36.211192, rho = -0.302170
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.171441
obj = -39.594257, rho = -0.320664
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.148111
obj = -43.051522, rho = -0.342109
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.127994
obj = -46.705722, rho = -0.306340
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.114451
obj = -49.404896, rho = -0.279301
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.095116
obj = -51.580555, rho = -0.299985
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.932580
obj = -6.729180, rho = -0.426616
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.860000
obj = -7.789620, rho = -0.411134
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.787459
obj = -8.951147, rho = -0.367179
nSV = 81, nBSV = 76
Total nSV = 81
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.720740
obj = -10.276694, rho = -0.348872
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.658452
obj = -11.685603, rho = -0.330698
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.589912
obj = -13.232058, rho = -0.365705
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.532834
obj = -14.915719, rho = -0.382567
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.470866
obj = -16.710509, rho = -0.402643
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.417479
obj = -18.693550, rho = -0.379821
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.359599
obj = -20.895650, rho = -0.386730
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.312204
obj = -23.491397, rho = -0.407822
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.277820
obj = -26.538558, rho = -0.384550
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.250234
obj = -29.915695, rho = -0.317336
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.226192
obj = -33.152938, rho = -0.210059
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.191750
obj = -36.787887, rho = -0.215287
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.168792
obj = -41.011495, rho = -0.173651
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.143089
obj = -45.888122, rho = -0.177122
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.124274
obj = -51.867237, rho = -0.169730
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.108811
obj = -59.157064, rho = -0.171497
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.097821
obj = -67.768832, rho = -0.160758
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -7.078843, rho = 0.235828
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 82% (82/100) (classification)
Accuracy = 81.8% (818/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.857714
obj = -8.433229, rho = 0.166383
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 92% (92/100) (classification)
Accuracy = 90.7% (907/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.820000
obj = -9.963272, rho = 0.093298
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.769969
obj = -11.654026, rho = 0.082900
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.718628
obj = -13.525201, rho = 0.093070
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.666019
obj = -15.554317, rho = 0.081007
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.602111
obj = -17.773064, rho = 0.038148
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.541110
obj = -20.305224, rho = 0.043593
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.484619
obj = -23.143356, rho = 0.057596
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.440000
obj = -26.359657, rho = 0.095365
nSV = 45, nBSV = 43
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.400000
obj = -29.814242, rho = 0.140613
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.365008
obj = -33.237907, rho = 0.192948
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.324248
obj = -36.491083, rho = 0.207316
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.275839
obj = -40.017410, rho = 0.201093
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.240973
obj = -43.785726, rho = 0.229287
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.207439
obj = -47.690170, rho = 0.227563
nSV = 26, nBSV = 15
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.175203
obj = -52.101121, rho = 0.282604
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.149376
obj = -57.227276, rho = 0.290192
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 189
nu = 0.126961
obj = -63.298769, rho = 0.298404
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 217
nu = 0.114780
obj = -69.302305, rho = 0.249496
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700000
obj = -5.836916, rho = -0.725070
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 78% (78/100) (classification)
Accuracy = 60.2% (602/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700000
obj = -7.031336, rho = -0.649664
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 90% (90/100) (classification)
Accuracy = 76.5% (765/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.680000
obj = -8.345620, rho = -0.598931
nSV = 68, nBSV = 68
Total nSV = 68
Accuracy = 95% (95/100) (classification)
Accuracy = 86.2% (862/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.659187
obj = -9.733212, rho = -0.519116
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 93% (930/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.618143
obj = -11.183356, rho = -0.469399
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.550051
obj = -12.745623, rho = -0.465292
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.492633
obj = -14.553184, rho = -0.443361
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.440363
obj = -16.600759, rho = -0.419598
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.399335
obj = -18.958746, rho = -0.506381
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.360000
obj = -21.573175, rho = -0.442951
nSV = 37, nBSV = 34
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.316637
obj = -24.509269, rho = -0.453536
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.288296
obj = -27.819038, rho = -0.497893
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.252253
obj = -31.623107, rho = -0.523431
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.223140
obj = -36.032573, rho = -0.520536
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.201723
obj = -41.209910, rho = -0.705791
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.182519
obj = -46.872122, rho = -0.654399
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.165082
obj = -53.160121, rho = -0.564461
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.154238
obj = -59.216843, rho = -0.334085
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.138276
obj = -64.597244, rho = -0.157324
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.119229
obj = -69.604725, rho = -0.296290
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -7.328774, rho = -0.216019
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.939130
obj = -8.572578, rho = -0.340241
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.860000
obj = -9.902220, rho = -0.304359
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.785639
obj = -11.422245, rho = -0.281213
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.723193
obj = -13.060023, rho = -0.330359
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.651677
obj = -14.863025, rho = -0.306831
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.582902
obj = -16.863466, rho = -0.325677
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.521997
obj = -19.085743, rho = -0.313088
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.464002
obj = -21.568326, rho = -0.339426
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.404799
obj = -24.465435, rho = -0.349653
nSV = 46, nBSV = 37
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.365507
obj = -27.879906, rho = -0.315530
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.323631
obj = -31.692371, rho = -0.302292
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.286593
obj = -36.099894, rho = -0.300595
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.256000
obj = -41.195038, rho = -0.240145
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.228126
obj = -47.139252, rho = -0.200220
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.208362
obj = -53.796011, rho = -0.300640
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.191503
obj = -60.908386, rho = -0.279951
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.170247
obj = -68.138781, rho = -0.212960
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.153804
obj = -75.736724, rho = -0.220052
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.138610
obj = -83.114699, rho = -0.330718
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -6.683973, rho = -0.472415
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 87% (87/100) (classification)
Accuracy = 83.4% (834/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.837614
obj = -7.917579, rho = -0.331413
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 94% (94/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.780000
obj = -9.248903, rho = -0.263934
nSV = 79, nBSV = 77
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.728697
obj = -10.737471, rho = -0.257745
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.673116
obj = -12.317320, rho = -0.218384
nSV = 70, nBSV = 65
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.616113
obj = -14.077776, rho = -0.223660
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.545839
obj = -16.018165, rho = -0.239050
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.488994
obj = -18.216966, rho = -0.202120
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.455370
obj = -20.533381, rho = -0.276364
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.394912
obj = -22.983312, rho = -0.296343
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.354511
obj = -25.797677, rho = -0.262846
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.305694
obj = -28.847495, rho = -0.269392
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.264678
obj = -32.492592, rho = -0.250159
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.236419
obj = -36.746331, rho = -0.285402
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.205154
obj = -41.581587, rho = -0.296297
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.182979
obj = -47.387647, rho = -0.339840
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*...*
optimization finished, #iter = 478
nu = 0.165693
obj = -53.774006, rho = -0.382626
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.145937
obj = -60.944091, rho = -0.379823
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.127357
obj = -69.394259, rho = -0.387930
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.116648
obj = -79.228076, rho = -0.461281
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -6.790924, rho = 0.184422
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 81% (81/100) (classification)
Accuracy = 79.1% (791/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -8.091130, rho = -0.039270
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 89% (89/100) (classification)
Accuracy = 91.9% (919/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.795305
obj = -9.477084, rho = -0.159612
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 90% (90/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.728136
obj = -11.057700, rho = -0.159595
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 94% (94/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.688386
obj = -12.841361, rho = -0.252784
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.618481
obj = -14.826164, rho = -0.237889
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.567205
obj = -17.121405, rho = -0.181256
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.512142
obj = -19.751327, rho = -0.210455
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.464397
obj = -22.767737, rho = -0.175708
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.426575
obj = -26.131262, rho = -0.180729
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.385022
obj = -29.784015, rho = -0.238955
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.352272
obj = -33.912072, rho = -0.105653
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.313375
obj = -38.359708, rho = -0.095411
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.282812
obj = -43.047755, rho = -0.117571
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.246378
obj = -48.364183, rho = -0.118494
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.225620
obj = -53.941471, rho = 0.017333
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.198401
obj = -59.555667, rho = 0.121513
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.168498
obj = -65.756915, rho = 0.167348
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.146980
obj = -72.853587, rho = 0.257990
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.130124
obj = -80.362523, rho = 0.308547
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.897260
obj = -6.564587, rho = -0.081751
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 96% (96/100) (classification)
Accuracy = 92.8% (928/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.849415
obj = -7.605411, rho = -0.154791
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.780000
obj = -8.720686, rho = -0.110643
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.698701
obj = -9.945045, rho = -0.093484
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.622352
obj = -11.375773, rho = -0.099067
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.562519
obj = -12.987765, rho = -0.089020
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.513303
obj = -14.767134, rho = -0.095225
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.463392
obj = -16.695413, rho = -0.066879
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.409964
obj = -18.766068, rho = -0.130086
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.364056
obj = -21.037964, rho = -0.155711
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.320887
obj = -23.510651, rho = -0.248889
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.276681
obj = -26.390055, rho = -0.216979
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.249944
obj = -29.651375, rho = -0.195202
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.224798
obj = -32.927042, rho = -0.249943
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.197609
obj = -36.143788, rho = -0.244495
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.175045
obj = -39.339010, rho = -0.159295
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.147266
obj = -42.413265, rho = -0.115625
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.131414
obj = -45.365140, rho = -0.193281
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.112489
obj = -47.003035, rho = -0.227942
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.092000
obj = -48.442469, rho = -0.220549
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -7.058702, rho = -0.573099
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 75% (75/100) (classification)
Accuracy = 69.5% (695/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.860000
obj = -8.456041, rho = -0.456011
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 90% (90/100) (classification)
Accuracy = 88% (880/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.821974
obj = -9.959929, rho = -0.390627
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 93.1% (931/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.781729
obj = -11.589298, rho = -0.303857
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.720000
obj = -13.386632, rho = -0.355820
nSV = 73, nBSV = 71
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.665125
obj = -15.278632, rho = -0.365368
nSV = 70, nBSV = 63
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.596714
obj = -17.392444, rho = -0.332511
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.532091
obj = -19.796594, rho = -0.315926
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.476952
obj = -22.521104, rho = -0.281852
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.421775
obj = -25.670267, rho = -0.270247
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.374427
obj = -29.313305, rho = -0.285197
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.336498
obj = -33.656073, rho = -0.359657
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.300619
obj = -38.563407, rho = -0.333703
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.277797
obj = -43.975763, rho = -0.307798
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.245862
obj = -50.021977, rho = -0.305565
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.215016
obj = -57.279995, rho = -0.253204
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.192284
obj = -66.113398, rho = -0.287443
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.175454
obj = -75.925790, rho = -0.419350
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.157586
obj = -87.638568, rho = -0.344358
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.142869
obj = -100.754886, rho = -0.335879
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.906045
obj = -7.049990, rho = 0.104541
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 95% (95/100) (classification)
Accuracy = 91.2% (912/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.874311
obj = -8.322203, rho = -0.024299
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.824869
obj = -9.734610, rho = -0.084834
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760284
obj = -11.292679, rho = -0.027080
nSV = 78, nBSV = 75
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.692535
obj = -13.073434, rho = 0.030810
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.644469
obj = -15.073109, rho = -0.018950
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.581559
obj = -17.235372, rho = 0.026566
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.523698
obj = -19.684320, rho = 0.062546
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.465533
obj = -22.545399, rho = 0.047619
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.415490
obj = -25.923010, rho = 0.099989
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.383910
obj = -29.706775, rho = 0.042648
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.346923
obj = -33.801809, rho = 0.151871
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.317952
obj = -38.125126, rho = 0.249917
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.283308
obj = -42.839184, rho = 0.350160
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.245408
obj = -47.890596, rho = 0.337309
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.215312
obj = -53.827641, rho = 0.411836
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.189295
obj = -60.658647, rho = 0.486085
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.176272
obj = -67.647103, rho = 0.633905
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.152386
obj = -74.149503, rho = 0.677930
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.137928
obj = -80.888358, rho = 0.749417
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.507599, rho = 0.051165
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.831635
obj = -7.523125, rho = 0.027139
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.777041
obj = -8.631700, rho = -0.000411
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.705614
obj = -9.770849, rho = 0.010805
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.636972
obj = -10.997741, rho = 0.048512
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.558736
obj = -12.329487, rho = 0.068773
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.497733
obj = -13.816212, rho = 0.056517
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.437674
obj = -15.389543, rho = 0.116157
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.378239
obj = -17.177751, rho = 0.116772
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.331074
obj = -19.287497, rho = 0.098798
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.289703
obj = -21.673855, rho = 0.135711
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.257288
obj = -24.446425, rho = 0.069110
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.229105
obj = -27.493156, rho = -0.045330
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.201657
obj = -30.893728, rho = -0.059159
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.175721
obj = -34.618186, rho = -0.063741
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.154287
obj = -39.052895, rho = -0.124240
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.135309
obj = -44.249890, rho = -0.113263
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.121304
obj = -50.144878, rho = -0.089115
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.105348
obj = -56.983256, rho = -0.098136
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.095875
obj = -65.072725, rho = 0.033885
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.895676
obj = -6.629674, rho = -0.352541
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 96% (96/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.827785
obj = -7.755641, rho = -0.312210
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.760515
obj = -9.044620, rho = -0.268238
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.708406
obj = -10.520566, rho = -0.281120
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.651859
obj = -12.162177, rho = -0.222295
nSV = 68, nBSV = 64
Total nSV = 68
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.598351
obj = -13.938621, rho = -0.150737
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.539329
obj = -15.965111, rho = -0.164392
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.484927
obj = -18.279186, rho = -0.147748
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.449275
obj = -20.793820, rho = -0.067547
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.400543
obj = -23.455862, rho = -0.087956
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.356731
obj = -26.285083, rho = -0.063917
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.309712
obj = -29.520944, rho = -0.065565
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.277729
obj = -33.107658, rho = -0.064863
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.246017
obj = -37.076782, rho = -0.169879
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.216452
obj = -41.162868, rho = -0.224808
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.188725
obj = -45.749834, rho = -0.217738
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.162528
obj = -51.041949, rho = -0.208000
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.142551
obj = -57.142809, rho = -0.136805
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*..*
optimization finished, #iter = 200
nu = 0.122214
obj = -64.279443, rho = -0.117214
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.110749
obj = -72.376189, rho = -0.180002
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.740000
obj = -6.382553, rho = -0.659654
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 68% (68/100) (classification)
Accuracy = 53.2% (532/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.740000
obj = -7.777528, rho = -0.566306
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 78% (78/100) (classification)
Accuracy = 70.7% (707/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.740000
obj = -9.333296, rho = -0.447355
nSV = 75, nBSV = 73
Total nSV = 75
Accuracy = 90% (90/100) (classification)
Accuracy = 87.6% (876/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.716753
obj = -10.984786, rho = -0.346209
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 93% (93/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.657915
obj = -12.854415, rho = -0.351492
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 93% (93/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.607133
obj = -15.018115, rho = -0.314565
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 94% (94/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.556359
obj = -17.534419, rho = -0.286165
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 94% (94/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.514784
obj = -20.488167, rho = -0.355433
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 94% (94/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.474875
obj = -23.836641, rho = -0.344000
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 94% (94/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.435010
obj = -27.694529, rho = -0.274953
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 94% (94/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.391125
obj = -32.209741, rho = -0.295667
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 94% (94/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.354539
obj = -37.585005, rho = -0.250130
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 95% (95/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.320298
obj = -44.055447, rho = -0.217497
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 95% (95/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.303080
obj = -51.624600, rho = -0.061854
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.275740
obj = -60.028439, rho = -0.010084
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.258197
obj = -69.515849, rho = -0.025905
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.227939
obj = -80.684983, rho = -0.011323
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.211570
obj = -93.678660, rho = 0.086755
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.190123
obj = -108.592973, rho = 0.058847
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.181916
obj = -125.418509, rho = 0.249454
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.903211
obj = -6.822962, rho = -0.282620
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.859915
obj = -7.993027, rho = -0.243138
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.802287
obj = -9.271931, rho = -0.252455
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.749452
obj = -10.624711, rho = -0.215834
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.670266
obj = -12.099286, rho = -0.239703
nSV = 71, nBSV = 65
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.608381
obj = -13.731752, rho = -0.187905
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.538706
obj = -15.546031, rho = -0.169176
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.476303
obj = -17.665419, rho = -0.192023
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.423856
obj = -20.133947, rho = -0.191583
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.373363
obj = -23.005223, rho = -0.201248
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.337342
obj = -26.301499, rho = -0.150672
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.303148
obj = -30.090420, rho = -0.121425
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.274532
obj = -34.401163, rho = -0.135171
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.250475
obj = -39.012528, rho = -0.223222
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.221801
obj = -43.989906, rho = -0.245966
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.198416
obj = -49.511947, rho = -0.314101
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.175063
obj = -55.604934, rho = -0.483685
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.159115
obj = -62.088295, rho = -0.459149
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.139809
obj = -68.476005, rho = -0.421740
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*..*
optimization finished, #iter = 287
nu = 0.124514
obj = -74.884691, rho = -0.413791
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.892235
obj = -6.335192, rho = -0.299359
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.818523
obj = -7.289864, rho = -0.273247
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.741760
obj = -8.353430, rho = -0.274568
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.677049
obj = -9.537057, rho = -0.239576
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.612925
obj = -10.795729, rho = -0.182226
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.547952
obj = -12.182567, rho = -0.175116
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.488193
obj = -13.693768, rho = -0.228211
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.428954
obj = -15.346115, rho = -0.205600
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.380000
obj = -17.245325, rho = -0.285343
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.334088
obj = -19.306925, rho = -0.332320
nSV = 35, nBSV = 32
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.293244
obj = -21.542411, rho = -0.324602
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.261728
obj = -24.003217, rho = -0.396909
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.228482
obj = -26.552308, rho = -0.431329
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.200822
obj = -29.293687, rho = -0.402427
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.175437
obj = -32.039737, rho = -0.352113
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.149572
obj = -35.124037, rho = -0.318128
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.132201
obj = -38.348718, rho = -0.276527
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.113102
obj = -41.645992, rho = -0.236548
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*
optimization finished, #iter = 343
nu = 0.096328
obj = -45.033871, rho = -0.204736
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.080297
obj = -48.889305, rho = -0.184488
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.930163
obj = -7.051132, rho = 0.096202
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.877045
obj = -8.278030, rho = 0.023463
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.804006
obj = -9.681393, rho = 0.047181
nSV = 84, nBSV = 78
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.747814
obj = -11.313832, rho = 0.119523
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.700000
obj = -13.136828, rho = 0.135802
nSV = 71, nBSV = 69
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.640349
obj = -15.153132, rho = 0.090571
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.591738
obj = -17.349710, rho = 0.027777
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.527846
obj = -19.772348, rho = 0.033362
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.465049
obj = -22.631715, rho = 0.010497
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.420207
obj = -25.915331, rho = -0.012474
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.376621
obj = -29.682544, rho = -0.028438
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.341299
obj = -34.010061, rho = 0.024303
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.303340
obj = -38.972234, rho = 0.038924
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.273613
obj = -44.863089, rho = 0.093396
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.244010
obj = -51.677876, rho = 0.105805
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.217024
obj = -59.901236, rho = 0.082163
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.200000
obj = -69.896423, rho = 0.024108
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.185008
obj = -80.612620, rho = -0.065307
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*
optimization finished, #iter = 265
nu = 0.165073
obj = -93.159009, rho = -0.086728
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*...*
optimization finished, #iter = 449
nu = 0.146011
obj = -108.602059, rho = -0.106120
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -6.902525, rho = 0.068974
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 87% (87/100) (classification)
Accuracy = 86.2% (862/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.862860
obj = -8.138442, rho = -0.103204
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 92% (92/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.829756
obj = -9.439038, rho = -0.168198
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 95% (95/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.750635
obj = -10.832416, rho = -0.208328
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 95% (95/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.684045
obj = -12.388446, rho = -0.158567
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.620564
obj = -14.067521, rho = -0.124514
nSV = 66, nBSV = 59
Total nSV = 66
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.548130
obj = -15.986091, rho = -0.151765
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.481132
obj = -18.245710, rho = -0.143284
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 95% (95/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.440205
obj = -20.785863, rho = -0.096361
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.398530
obj = -23.521764, rho = -0.060689
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.353061
obj = -26.572985, rho = -0.110594
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.313539
obj = -29.969291, rho = -0.141868
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.272671
obj = -33.912830, rho = -0.197348
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.247686
obj = -38.430750, rho = -0.307122
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.216021
obj = -43.405664, rho = -0.300147
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.192188
obj = -49.338773, rho = -0.312679
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 200
nu = 0.176795
obj = -55.477387, rho = -0.274667
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*...*
optimization finished, #iter = 368
nu = 0.153524
obj = -62.145432, rho = -0.283795
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.139695
obj = -69.247851, rho = -0.350733
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.126135
obj = -76.339141, rho = -0.444012
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.900000
obj = -7.130757, rho = 0.235845
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 86% (86/100) (classification)
Accuracy = 82.3% (823/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -8.433242, rho = 0.026257
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 97% (97/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.842783
obj = -9.791423, rho = -0.027563
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.772501
obj = -11.301202, rho = -0.063985
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.715292
obj = -12.990475, rho = -0.063136
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.653821
obj = -14.762999, rho = -0.019175
nSV = 68, nBSV = 62
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.583246
obj = -16.673808, rho = 0.068006
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.518583
obj = -18.777874, rho = 0.049288
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.467139
obj = -21.065142, rho = -0.016303
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.401526
obj = -23.660944, rho = -0.017799
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 173
nu = 0.352831
obj = -26.727944, rho = -0.009881
nSV = 40, nBSV = 31
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.308420
obj = -30.399965, rho = 0.002971
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.283473
obj = -34.344889, rho = -0.096589
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.250609
obj = -38.743145, rho = -0.108823
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.223620
obj = -43.413129, rho = -0.148440
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.196834
obj = -48.742114, rho = -0.102067
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.180699
obj = -54.016567, rho = -0.088265
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.153960
obj = -59.261468, rho = -0.051031
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.130392
obj = -65.730413, rho = -0.042707
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.112758
obj = -73.143875, rho = -0.041289
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940818
obj = -6.906919, rho = -0.073019
nSV = 96, nBSV = 94
Total nSV = 96
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.868571
obj = -8.063480, rho = -0.059659
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.799560
obj = -9.368518, rho = -0.038009
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.725336
obj = -10.887559, rho = -0.033769
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.668347
obj = -12.622311, rho = 0.013564
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.623103
obj = -14.541210, rho = 0.105872
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.574407
obj = -16.542590, rho = 0.090680
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.510265
obj = -18.682902, rho = 0.079776
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.460000
obj = -20.986821, rho = 0.126829
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.407834
obj = -23.453576, rho = 0.084190
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.352952
obj = -26.281780, rho = 0.105726
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.307181
obj = -29.697510, rho = 0.092396
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.268158
obj = -33.791886, rho = 0.085390
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.242870
obj = -38.377469, rho = 0.137528
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.216656
obj = -43.489447, rho = 0.131090
nSV = 27, nBSV = 16
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.187278
obj = -49.618926, rho = 0.125973
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.166701
obj = -57.167196, rho = 0.100916
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.148871
obj = -66.056419, rho = 0.066999
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.140767
obj = -76.013378, rho = -0.070459
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.127576
obj = -86.616111, rho = -0.101950
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -6.674714, rho = -0.554395
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 75% (75/100) (classification)
Accuracy = 70.9% (709/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -7.972331, rho = -0.432176
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 92% (92/100) (classification)
Accuracy = 89.6% (896/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.801737
obj = -9.304427, rho = -0.343105
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.748804
obj = -10.669955, rho = -0.267008
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.680488
obj = -12.175002, rho = -0.266910
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.609748
obj = -13.807323, rho = -0.267391
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.553679
obj = -15.552605, rho = -0.235373
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.484031
obj = -17.469529, rho = -0.242472
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.426020
obj = -19.750877, rho = -0.208224
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.379364
obj = -22.202179, rho = -0.216567
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.338773
obj = -24.944483, rho = -0.173749
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.297667
obj = -27.933109, rho = -0.134796
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.260912
obj = -31.289816, rho = -0.124555
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.230807
obj = -34.909838, rho = -0.229560
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.197513
obj = -39.191360, rho = -0.226088
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.173709
obj = -44.343475, rho = -0.259856
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.154156
obj = -50.223393, rho = -0.302010
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.137915
obj = -56.921580, rho = -0.285307
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.121555
obj = -64.336250, rho = -0.346246
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*..*
optimization finished, #iter = 207
nu = 0.110724
obj = -72.212188, rho = -0.540326
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.290433, rho = -0.491539
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 86% (86/100) (classification)
Accuracy = 86.6% (866/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -8.637150, rho = -0.401442
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 96% (96/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.856848
obj = -10.073175, rho = -0.323952
nSV = 87, nBSV = 84
Total nSV = 87
Accuracy = 95% (95/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.792644
obj = -11.684756, rho = -0.271121
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.723357
obj = -13.464364, rho = -0.269905
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.650322
obj = -15.514721, rho = -0.245131
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.587206
obj = -17.937974, rho = -0.218609
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.535235
obj = -20.756656, rho = -0.207765
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.495277
obj = -23.872175, rho = -0.149144
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.447421
obj = -27.323246, rho = -0.184260
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.406580
obj = -31.103667, rho = -0.221783
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.359856
obj = -35.338450, rho = -0.225376
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.322674
obj = -40.157506, rho = -0.299973
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.289718
obj = -45.629632, rho = -0.339991
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.256099
obj = -51.683570, rho = -0.364899
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.224422
obj = -58.925142, rho = -0.337349
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.204351
obj = -67.293016, rho = -0.303562
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.184303
obj = -76.367932, rho = -0.287019
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.171535
obj = -85.740636, rho = -0.325976
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 179
nu = 0.152853
obj = -94.121545, rho = -0.337805
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.930789
obj = -7.228420, rho = 0.030117
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 89% (89/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.892612
obj = -8.540468, rho = 0.043489
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 95% (95/100) (classification)
Accuracy = 93.3% (933/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -10.011349, rho = 0.021903
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.782938
obj = -11.623912, rho = -0.018548
nSV = 80, nBSV = 75
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.719823
obj = -13.445858, rho = -0.120815
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.668756
obj = -15.459108, rho = -0.253720
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.603669
obj = -17.624340, rho = -0.192230
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.557449
obj = -19.986272, rho = -0.184279
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.490689
obj = -22.398201, rho = -0.168696
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.436815
obj = -25.117216, rho = -0.199223
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.385650
obj = -28.001764, rho = -0.186310
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.336110
obj = -31.198896, rho = -0.184209
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.295838
obj = -34.845018, rho = -0.081490
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.259510
obj = -38.630649, rho = -0.074225
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.227505
obj = -42.910874, rho = -0.084505
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.193689
obj = -47.665017, rho = -0.073482
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.174937
obj = -52.939142, rho = 0.015548
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.152178
obj = -58.057684, rho = 0.068047
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*......*
optimization finished, #iter = 682
nu = 0.129896
obj = -63.761117, rho = 0.034705
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 289
nu = 0.111462
obj = -70.595332, rho = 0.050910
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.917692
obj = -6.708090, rho = -0.160982
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 96% (96/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.841607
obj = -7.817815, rho = -0.122797
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 96% (96/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.793440
obj = -9.030726, rho = -0.138606
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.727057
obj = -10.326410, rho = -0.121371
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.653347
obj = -11.768129, rho = -0.127593
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.581602
obj = -13.374633, rho = -0.120482
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.520000
obj = -15.242994, rho = -0.146480
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.466563
obj = -17.332895, rho = -0.179396
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.412528
obj = -19.739714, rho = -0.171421
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.369136
obj = -22.550824, rho = -0.165717
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.328289
obj = -25.829020, rho = -0.162669
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.304575
obj = -29.461672, rho = -0.175877
nSV = 33, nBSV = 29
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.274320
obj = -33.179454, rho = -0.188197
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.240421
obj = -37.303411, rho = -0.188326
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 291
nu = 0.207868
obj = -42.260173, rho = -0.207087
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.181606
obj = -48.366230, rho = -0.174872
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.161051
obj = -55.766449, rho = -0.166653
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.148902
obj = -64.554942, rho = -0.207787
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.139731
obj = -73.684755, rho = -0.284785
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.125956
obj = -83.158235, rho = -0.240418
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.932192
obj = -7.002889, rho = -0.149008
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.211921, rho = -0.192415
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.818874
obj = -9.543610, rho = -0.174833
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.752850
obj = -11.027428, rho = -0.107501
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.675278
obj = -12.734691, rho = -0.102636
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.637058
obj = -14.627876, rho = -0.076115
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.575638
obj = -16.657781, rho = -0.091050
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.515245
obj = -18.844254, rho = -0.070566
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.453219
obj = -21.343778, rho = -0.079813
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.401754
obj = -24.249717, rho = -0.143609
nSV = 46, nBSV = 36
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.357950
obj = -27.641089, rho = -0.133520
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.325775
obj = -31.327861, rho = -0.037626
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.289261
obj = -35.442690, rho = -0.011713
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.254104
obj = -40.140207, rho = 0.012533
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.231286
obj = -45.505067, rho = 0.108900
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.200313
obj = -51.364347, rho = 0.106641
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.177803
obj = -58.410443, rho = 0.125081
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.162657
obj = -66.012179, rho = 0.103802
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.150834
obj = -73.392629, rho = 0.149363
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.127280
obj = -80.959680, rho = 0.166956
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.928225
obj = -6.812597, rho = -0.176529
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.861115
obj = -7.922670, rho = -0.182385
nSV = 88, nBSV = 85
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.797730
obj = -9.166567, rho = -0.151710
nSV = 82, nBSV = 78
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.732921
obj = -10.537676, rho = -0.170350
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.666850
obj = -12.061511, rho = -0.184190
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.589939
obj = -13.782648, rho = -0.163713
nSV = 64, nBSV = 58
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.529561
obj = -15.804710, rho = -0.145483
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.483491
obj = -18.096213, rho = -0.075524
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.435234
obj = -20.611376, rho = -0.062565
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.388889
obj = -23.469055, rho = -0.040315
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.353388
obj = -26.699296, rho = -0.081805
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.313189
obj = -30.128157, rho = -0.061789
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.279607
obj = -33.989604, rho = -0.068652
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.244843
obj = -38.350381, rho = -0.035677
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.219085
obj = -43.434353, rho = -0.009678
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.189746
obj = -49.137615, rho = 0.013717
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.164431
obj = -56.333433, rho = 0.015479
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.145917
obj = -65.305224, rho = 0.028359
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.133400
obj = -75.964405, rho = 0.031374
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.125900
obj = -87.850224, rho = 0.006560
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -7.465153, rho = -0.425200
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 72% (72/100) (classification)
Accuracy = 77.3% (773/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -8.976226, rho = -0.267547
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 92% (92/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.876514
obj = -10.610921, rho = -0.149509
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.812309
obj = -12.439241, rho = -0.095467
nSV = 85, nBSV = 80
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.763220
obj = -14.493656, rho = -0.067095
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.693326
obj = -16.806040, rho = -0.030264
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.640000
obj = -19.501848, rho = 0.009689
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.588307
obj = -22.340885, rho = 0.074390
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.529813
obj = -25.620067, rho = 0.089072
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.467912
obj = -29.415120, rho = 0.098473
nSV = 52, nBSV = 42
Total nSV = 52
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.425056
obj = -33.944463, rho = 0.104706
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.393059
obj = -39.139471, rho = 0.161547
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.353981
obj = -44.722874, rho = 0.135880
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.325579
obj = -50.925647, rho = 0.147180
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.285383
obj = -57.720482, rho = 0.117793
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.259440
obj = -65.361175, rho = 0.097277
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.229600
obj = -73.663690, rho = 0.065777
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.211084
obj = -82.181334, rho = -0.025708
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.184370
obj = -90.834434, rho = 0.098995
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.158014
obj = -100.483420, rho = 0.092118
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.929234
obj = -6.957383, rho = -0.116633
nSV = 94, nBSV = 91
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.882696
obj = -8.122288, rho = -0.140330
nSV = 90, nBSV = 87
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.819885
obj = -9.379233, rho = -0.164623
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.743485
obj = -10.792339, rho = -0.109083
nSV = 77, nBSV = 72
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.683030
obj = -12.356922, rho = -0.113845
nSV = 70, nBSV = 66
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.606554
obj = -14.118441, rho = -0.176223
nSV = 64, nBSV = 56
Total nSV = 64
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.542368
obj = -16.202404, rho = -0.183343
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.500733
obj = -18.457714, rho = -0.167802
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.454301
obj = -20.859147, rho = -0.123637
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.398958
obj = -23.490654, rho = -0.061471
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.352851
obj = -26.529739, rho = -0.100612
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.314038
obj = -29.899757, rho = -0.107024
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.275154
obj = -33.678765, rho = -0.095809
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.243263
obj = -38.103184, rho = -0.029025
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.213145
obj = -43.277886, rho = -0.035356
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.193110
obj = -49.194816, rho = -0.006798
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.169864
obj = -55.725684, rho = 0.014297
nSV = 23, nBSV = 12
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.148755
obj = -63.581746, rho = -0.008073
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.133027
obj = -72.967906, rho = -0.032729
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.120000
obj = -83.734727, rho = -0.093339
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.920000
obj = -6.733053, rho = -0.283071
nSV = 92, nBSV = 92
Total nSV = 92
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.868973
obj = -7.791938, rho = -0.218757
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.794318
obj = -8.934689, rho = -0.213668
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.728141
obj = -10.196791, rho = -0.207539
nSV = 74, nBSV = 69
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.658407
obj = -11.584302, rho = -0.230747
nSV = 69, nBSV = 62
Total nSV = 69
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.582438
obj = -13.109475, rho = -0.175860
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.541053
obj = -14.659861, rho = -0.269797
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.478621
obj = -16.201012, rho = -0.227195
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.413360
obj = -17.782938, rho = -0.205153
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.350031
obj = -19.593096, rho = -0.223953
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.305659
obj = -21.663178, rho = -0.265057
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.263259
obj = -23.935230, rho = -0.280093
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.227773
obj = -26.529934, rho = -0.358001
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.194283
obj = -29.578393, rho = -0.368651
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.174595
obj = -32.875802, rho = -0.338106
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.152716
obj = -36.351984, rho = -0.318258
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.134340
obj = -40.160737, rho = -0.338520
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.118646
obj = -43.630364, rho = -0.333934
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.101062
obj = -47.149669, rho = -0.319871
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 201
nu = 0.084118
obj = -51.064934, rho = -0.337653
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -6.926577, rho = -0.018375
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.875356
obj = -8.070755, rho = -0.047201
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.811851
obj = -9.353481, rho = -0.028480
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.755452
obj = -10.757570, rho = -0.022827
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.678094
obj = -12.281542, rho = -0.029259
nSV = 69, nBSV = 65
Total nSV = 69
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.608348
obj = -14.018293, rho = -0.049889
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.556975
obj = -15.905712, rho = -0.073522
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.493376
obj = -17.922282, rho = -0.064538
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.437061
obj = -20.229181, rho = -0.036169
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.389832
obj = -22.847969, rho = -0.048582
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.342939
obj = -25.774732, rho = -0.024146
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.305569
obj = -28.975972, rho = -0.021745
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.267535
obj = -32.678481, rho = -0.049117
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.236277
obj = -36.790313, rho = -0.113133
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.204237
obj = -41.774238, rho = -0.118210
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.185515
obj = -47.637869, rho = -0.143707
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 196
nu = 0.166038
obj = -54.043697, rho = -0.173161
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*......*
optimization finished, #iter = 804
nu = 0.144005
obj = -61.570593, rho = -0.183010
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.127685
obj = -70.767663, rho = -0.185740
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.116482
obj = -81.239856, rho = -0.223399
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.886165
obj = -6.616672, rho = -0.267985
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 97% (97/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.838052
obj = -7.735831, rho = -0.196611
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 97% (97/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.778075
obj = -8.945664, rho = -0.150574
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.713863
obj = -10.288555, rho = -0.135006
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.652606
obj = -11.720139, rho = -0.051365
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.587099
obj = -13.309124, rho = -0.096771
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.519931
obj = -15.079011, rho = -0.081968
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.463219
obj = -17.138946, rho = -0.101935
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.423201
obj = -19.361256, rho = -0.131210
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.371942
obj = -21.715297, rho = -0.151458
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.331959
obj = -24.332034, rho = -0.204918
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.286143
obj = -27.297293, rho = -0.198030
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.251707
obj = -30.802183, rho = -0.212239
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.220449
obj = -34.830556, rho = -0.240789
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.199990
obj = -39.463422, rho = -0.263655
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.177966
obj = -44.262218, rho = -0.351917
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.155906
obj = -49.786599, rho = -0.391528
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.133297
obj = -56.406924, rho = -0.386161
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.118040
obj = -64.690975, rho = -0.388991
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.105575
obj = -74.152425, rho = -0.381177
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.895708
obj = -6.780756, rho = -0.502142
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 93% (93/100) (classification)
Accuracy = 91.5% (915/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -7.973746, rho = -0.442109
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 95% (95/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.787193
obj = -9.318973, rho = -0.413671
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.733201
obj = -10.822730, rho = -0.343854
nSV = 74, nBSV = 71
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.681434
obj = -12.439635, rho = -0.392217
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.604070
obj = -14.229477, rho = -0.378284
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.547154
obj = -16.301757, rho = -0.344798
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.485127
obj = -18.720213, rho = -0.310063
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.434928
obj = -21.596081, rho = -0.305019
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.395938
obj = -25.022982, rho = -0.310755
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.367289
obj = -28.734537, rho = -0.269090
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.329031
obj = -32.813384, rho = -0.269744
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.297280
obj = -37.553063, rho = -0.289993
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.267664
obj = -42.897543, rho = -0.304539
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.240052
obj = -48.995963, rho = -0.240977
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.217743
obj = -55.664636, rho = -0.198501
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.195504
obj = -62.967561, rho = -0.253034
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.177189
obj = -70.685147, rho = -0.338552
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.156641
obj = -78.948127, rho = -0.294574
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.138314
obj = -87.559358, rho = -0.366443
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.978031
obj = -7.309526, rho = -0.184316
nSV = 98, nBSV = 96
Total nSV = 98
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.922223
obj = -8.543874, rho = -0.173922
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.879602
obj = -9.854675, rho = -0.085768
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.812442
obj = -11.202782, rho = -0.097436
nSV = 82, nBSV = 79
Total nSV = 82
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.738953
obj = -12.569134, rho = -0.075813
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.652080
obj = -13.987156, rho = -0.092569
nSV = 67, nBSV = 63
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.569305
obj = -15.527334, rho = -0.088857
nSV = 62, nBSV = 54
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.491431
obj = -17.242547, rho = -0.108910
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.430706
obj = -19.208560, rho = -0.108584
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.378615
obj = -21.368182, rho = -0.060290
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.327247
obj = -23.732404, rho = -0.089972
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.289705
obj = -26.344892, rho = -0.073755
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.251296
obj = -29.185194, rho = -0.114647
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.217433
obj = -32.317963, rho = -0.103850
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.187755
obj = -35.934722, rho = -0.080112
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.162248
obj = -40.113173, rho = -0.086875
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.143076
obj = -44.914828, rho = -0.155826
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.127359
obj = -50.093690, rho = -0.197953
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 311
nu = 0.114931
obj = -55.262507, rho = -0.252137
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.096844
obj = -60.549383, rho = -0.260676
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -6.823223, rho = -0.005019
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 93% (93/100) (classification)
Accuracy = 91.2% (912/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.859443
obj = -7.994014, rho = 0.002862
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 93% (93/100) (classification)
Accuracy = 94% (940/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.800000
obj = -9.295109, rho = 0.035129
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 94% (94/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.723097
obj = -10.756936, rho = 0.006870
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 94% (94/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.665132
obj = -12.432092, rho = 0.035300
nSV = 71, nBSV = 63
Total nSV = 71
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.610358
obj = -14.313362, rho = 0.024506
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.554176
obj = -16.391772, rho = 0.073698
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 96% (96/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.497796
obj = -18.724317, rho = 0.134573
nSV = 54, nBSV = 45
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.446137
obj = -21.442635, rho = 0.142580
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.401461
obj = -24.455039, rho = 0.176621
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.356957
obj = -27.946102, rho = 0.185546
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.314144
obj = -32.127256, rho = 0.201519
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 187
nu = 0.284989
obj = -37.135082, rho = 0.243315
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.258100
obj = -42.900672, rho = 0.208118
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.233044
obj = -49.498523, rho = 0.211458
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.205605
obj = -57.573840, rho = 0.198734
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.194278
obj = -67.139932, rho = 0.337157
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.175738
obj = -77.447674, rho = 0.409412
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.160928
obj = -89.534057, rho = 0.493400
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.145055
obj = -103.077370, rho = 0.577384
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.895641
obj = -6.592452, rho = 0.020472
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 94% (94/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.833219
obj = -7.670141, rho = -0.032707
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.773737
obj = -8.887641, rho = -0.090719
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 95% (95/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.706007
obj = -10.232445, rho = -0.028847
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.640000
obj = -11.753138, rho = -0.014189
nSV = 65, nBSV = 63
Total nSV = 65
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.580135
obj = -13.462660, rho = -0.080296
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.520917
obj = -15.345992, rho = -0.116317
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.466575
obj = -17.556746, rho = -0.163723
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.421411
obj = -19.986502, rho = -0.141915
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.370647
obj = -22.818174, rho = -0.141276
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.331989
obj = -26.196907, rho = -0.145569
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.299896
obj = -30.048591, rho = -0.202207
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.268781
obj = -34.505657, rho = -0.214844
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.244256
obj = -39.608234, rho = -0.161060
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.218142
obj = -45.297123, rho = -0.183645
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.191443
obj = -52.298850, rho = -0.177368
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.169896
obj = -60.915376, rho = -0.144115
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.153926
obj = -71.596654, rho = -0.084422
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.141826
obj = -84.459240, rho = -0.027221
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.134260
obj = -99.235228, rho = 0.059077
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.908092
obj = -6.869625, rho = -0.086410
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.047722, rho = -0.133801
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -9.344897, rho = -0.113341
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.757065
obj = -10.731070, rho = -0.126206
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.688510
obj = -12.183447, rho = -0.111395
nSV = 72, nBSV = 66
Total nSV = 72
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.608914
obj = -13.768456, rho = -0.112846
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.542859
obj = -15.602452, rho = -0.142828
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.484455
obj = -17.584368, rho = -0.162663
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.433046
obj = -19.804420, rho = -0.134872
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.387221
obj = -22.209033, rho = -0.127288
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.348946
obj = -24.526389, rho = -0.102898
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.304258
obj = -26.949149, rho = -0.105575
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.261998
obj = -29.587456, rho = -0.122551
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.227866
obj = -32.257768, rho = -0.142591
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.195558
obj = -35.042516, rho = -0.140998
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.165144
obj = -38.240834, rho = -0.142417
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.148934
obj = -41.273020, rho = -0.196044
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.127927
obj = -43.608297, rho = -0.230130
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.105854
obj = -45.514797, rho = -0.255159
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.085766
obj = -47.684750, rho = -0.246316
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.941794
obj = -7.045383, rho = -0.168016
nSV = 96, nBSV = 92
Total nSV = 96
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -8.257314, rho = -0.187993
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.836493
obj = -9.562779, rho = -0.227615
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.769585
obj = -10.962810, rho = -0.187664
nSV = 79, nBSV = 73
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.698983
obj = -12.508575, rho = -0.114061
nSV = 72, nBSV = 67
Total nSV = 72
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.617387
obj = -14.241105, rho = -0.122503
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.557750
obj = -16.225029, rho = -0.138472
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.511483
obj = -18.290531, rho = -0.069491
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.444810
obj = -20.580095, rho = -0.084306
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.394142
obj = -23.195068, rho = -0.132746
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.351435
obj = -26.090414, rho = -0.140196
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.309849
obj = -29.183818, rho = -0.123102
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.271491
obj = -32.775330, rho = -0.177237
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.235941
obj = -36.915410, rho = -0.189070
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.208752
obj = -41.809596, rho = -0.168307
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.183765
obj = -47.360057, rho = -0.123554
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.165553
obj = -53.556508, rho = -0.106283
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.146899
obj = -60.439545, rho = -0.076000
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.129006
obj = -68.524365, rho = -0.097983
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.116122
obj = -77.535706, rho = -0.145254
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.078939, rho = -0.247318
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 96% (96/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.899227
obj = -8.297312, rho = -0.108198
nSV = 91, nBSV = 88
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -9.620037, rho = -0.059750
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.783030
obj = -11.005716, rho = -0.005322
nSV = 80, nBSV = 77
Total nSV = 80
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.699937
obj = -12.457976, rho = 0.018900
nSV = 74, nBSV = 67
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.632391
obj = -14.101408, rho = 0.056809
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.564739
obj = -15.862568, rho = 0.052265
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.498305
obj = -17.778798, rho = 0.102569
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.438235
obj = -19.951855, rho = 0.103290
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.385677
obj = -22.440984, rho = 0.090598
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.344762
obj = -25.065625, rho = 0.035094
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.304165
obj = -27.832141, rho = 0.049065
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.266302
obj = -30.795457, rho = 0.034529
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.231361
obj = -34.149095, rho = 0.088289
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.201879
obj = -37.741935, rho = 0.141614
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.170873
obj = -41.851893, rho = 0.162887
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.149744
obj = -46.717227, rho = 0.199403
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.133019
obj = -52.099011, rho = 0.196631
nSV = 15, nBSV = 11
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.120879
obj = -56.815934, rho = 0.135469
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.105609
obj = -61.467573, rho = 0.133479
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -6.835300, rho = -0.375239
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 91% (91/100) (classification)
Accuracy = 91.7% (917/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.869366
obj = -7.986819, rho = -0.272352
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.801402
obj = -9.235465, rho = -0.229968
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.746896
obj = -10.592092, rho = -0.200989
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.684365
obj = -12.004418, rho = -0.192403
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.606876
obj = -13.503516, rho = -0.150855
nSV = 64, nBSV = 57
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.540000
obj = -15.221895, rho = -0.107710
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.475143
obj = -17.070035, rho = -0.098466
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.427887
obj = -19.065338, rho = -0.045010
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.372466
obj = -21.240298, rho = -0.013577
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.334501
obj = -23.533182, rho = -0.136212
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.291325
obj = -25.823012, rho = -0.170285
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.257612
obj = -28.193617, rho = -0.170043
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 189
nu = 0.216229
obj = -30.588448, rho = -0.162497
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.181987
obj = -33.375942, rho = -0.184407
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.155651
obj = -36.675765, rho = -0.170080
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.137007
obj = -40.135392, rho = -0.236656
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.120624
obj = -43.409793, rho = -0.296514
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.102337
obj = -46.248922, rho = -0.321178
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.087079
obj = -49.178129, rho = -0.369267
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.920000
obj = -7.065280, rho = -0.285520
nSV = 93, nBSV = 91
Total nSV = 93
Accuracy = 94% (94/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.860000
obj = -8.352576, rho = -0.219785
nSV = 86, nBSV = 86
Total nSV = 86
Accuracy = 95% (95/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.820000
obj = -9.793954, rho = -0.194662
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.765181
obj = -11.362239, rho = -0.154410
nSV = 79, nBSV = 74
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.711966
obj = -13.100401, rho = -0.145159
nSV = 73, nBSV = 70
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.650357
obj = -14.937374, rho = -0.108482
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.583459
obj = -17.000557, rho = -0.068143
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.522562
obj = -19.271570, rho = -0.029173
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.462847
obj = -21.928456, rho = -0.008643
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.416615
obj = -24.939655, rho = 0.022423
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.364382
obj = -28.386103, rho = 0.037795
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.328590
obj = -32.498935, rho = -0.037571
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.295581
obj = -37.118692, rho = -0.054301
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.260989
obj = -42.484745, rho = -0.034905
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.239313
obj = -48.496048, rho = 0.001036
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.214579
obj = -55.151406, rho = 0.008994
nSV = 23, nBSV = 19
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.191791
obj = -62.396404, rho = -0.030868
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.165914
obj = -71.154783, rho = -0.022064
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.151356
obj = -81.279785, rho = -0.005149
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.140220
obj = -91.937167, rho = 0.237185
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -6.477490, rho = -0.575892
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 90% (90/100) (classification)
Accuracy = 73.7% (737/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.820000
obj = -7.652084, rho = -0.461709
nSV = 82, nBSV = 82
Total nSV = 82
Accuracy = 95% (95/100) (classification)
Accuracy = 88.2% (882/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.777697
obj = -8.872168, rho = -0.373763
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.703500
obj = -10.182928, rho = -0.315697
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.646146
obj = -11.631155, rho = -0.360956
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.575701
obj = -13.231908, rho = -0.357245
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.524904
obj = -15.057254, rho = -0.297567
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.474936
obj = -16.943963, rho = -0.357927
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.412275
obj = -19.043038, rho = -0.370418
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.365235
obj = -21.511089, rho = -0.331178
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.322324
obj = -24.218061, rho = -0.305127
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.280573
obj = -27.456458, rho = -0.329088
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.250717
obj = -31.210837, rho = -0.294331
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.222333
obj = -35.618060, rho = -0.263460
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.202967
obj = -40.313363, rho = -0.202012
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.186152
obj = -45.336473, rho = -0.118458
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.164395
obj = -50.297525, rho = -0.154582
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.143868
obj = -55.792007, rho = -0.183863
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.127672
obj = -61.451855, rho = -0.140632
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.109050
obj = -67.169673, rho = -0.211270
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.895503
obj = -6.556042, rho = -0.380344
nSV = 91, nBSV = 87
Total nSV = 91
Accuracy = 98% (98/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.830774
obj = -7.635695, rho = -0.340950
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.772475
obj = -8.828002, rho = -0.374049
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.727648
obj = -10.096095, rho = -0.392023
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.650107
obj = -11.405933, rho = -0.389056
nSV = 66, nBSV = 63
Total nSV = 66
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.576776
obj = -12.847430, rho = -0.377386
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.516611
obj = -14.460710, rho = -0.405731
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.460452
obj = -16.115740, rho = -0.361775
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.401505
obj = -17.933306, rho = -0.381936
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.354070
obj = -19.904831, rho = -0.357915
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.309542
obj = -21.994907, rho = -0.322023
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.267727
obj = -24.287398, rho = -0.301544
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.237721
obj = -26.765201, rho = -0.301592
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.208916
obj = -29.034324, rho = -0.245209
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.178144
obj = -31.342060, rho = -0.213377
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 484
nu = 0.148959
obj = -33.799662, rho = -0.214713
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.129410
obj = -36.515255, rho = -0.271221
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.114859
obj = -38.638808, rho = -0.326748
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.094387
obj = -40.146099, rho = -0.345381
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 383
nu = 0.076559
obj = -41.665027, rho = -0.363735
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.903774, rho = -0.125435
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 89% (89/100) (classification)
Accuracy = 86.6% (866/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.851645
obj = -8.152824, rho = -0.271361
nSV = 86, nBSV = 83
Total nSV = 86
Accuracy = 93% (93/100) (classification)
Accuracy = 90.8% (908/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.792436
obj = -9.564622, rho = -0.321190
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 94% (94/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.733757
obj = -11.178028, rho = -0.312382
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 94% (94/100) (classification)
Accuracy = 93.5% (935/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.691162
obj = -12.996845, rho = -0.308140
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 94% (94/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.638607
obj = -14.970826, rho = -0.279661
nSV = 65, nBSV = 62
Total nSV = 65
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.584006
obj = -17.126030, rho = -0.241480
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.526509
obj = -19.512988, rho = -0.201710
nSV = 55, nBSV = 52
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.480000
obj = -22.004988, rho = -0.214901
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.430660
obj = -24.602444, rho = -0.277725
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.370260
obj = -27.470767, rho = -0.286156
nSV = 43, nBSV = 33
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.326422
obj = -30.956520, rho = -0.287855
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.287517
obj = -34.732875, rho = -0.302611
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.255714
obj = -38.900553, rho = -0.259863
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.223339
obj = -43.664563, rho = -0.288624
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.198421
obj = -48.914689, rho = -0.314281
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.174929
obj = -54.617322, rho = -0.433770
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.156186
obj = -60.542724, rho = -0.502960
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.136229
obj = -66.958464, rho = -0.520275
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.118959
obj = -73.909878, rho = -0.532186
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -7.062875, rho = -0.121986
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 96% (96/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.896764
obj = -8.256425, rho = -0.207054
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.843605
obj = -9.537360, rho = -0.229899
nSV = 86, nBSV = 82
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.769071
obj = -10.915462, rho = -0.221130
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.700000
obj = -12.418455, rho = -0.211464
nSV = 73, nBSV = 69
Total nSV = 73
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.628588
obj = -14.016503, rho = -0.177397
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.557979
obj = -15.824365, rho = -0.173799
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.491592
obj = -17.823713, rho = -0.191757
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.437827
obj = -20.076530, rho = -0.197712
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.385696
obj = -22.634810, rho = -0.232823
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.340354
obj = -25.487956, rho = -0.245899
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.305905
obj = -28.672579, rho = -0.206562
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.267200
obj = -32.162850, rho = -0.209824
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.243323
obj = -35.822868, rho = -0.274016
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.214970
obj = -39.343285, rho = -0.267534
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.184941
obj = -43.066824, rho = -0.281818
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.164478
obj = -46.588417, rho = -0.320129
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.136579
obj = -50.179799, rho = -0.275073
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.114724
obj = -54.317360, rho = -0.278087
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.096074
obj = -59.273996, rho = -0.287297
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.976797
obj = -7.213295, rho = -0.136447
nSV = 98, nBSV = 95
Total nSV = 98
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.901661
obj = -8.428175, rho = -0.071070
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.840000
obj = -9.793009, rho = -0.046645
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.776631
obj = -11.302779, rho = 0.011044
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.702971
obj = -12.984540, rho = -0.039099
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.640452
obj = -14.851842, rho = -0.092291
nSV = 68, nBSV = 61
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.579039
obj = -16.953949, rho = -0.102522
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.525652
obj = -19.274683, rho = -0.078545
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.468258
obj = -21.805970, rho = -0.012800
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.412487
obj = -24.680062, rho = 0.005476
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.366143
obj = -27.977294, rho = 0.028743
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.322782
obj = -31.804599, rho = 0.056058
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.295892
obj = -36.182311, rho = 0.037435
nSV = 31, nBSV = 28
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.270835
obj = -40.647120, rho = 0.074063
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.241967
obj = -45.104883, rho = 0.053207
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.211957
obj = -49.723683, rho = 0.117220
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 281
nu = 0.182500
obj = -54.270955, rho = 0.144410
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.154683
obj = -59.758991, rho = 0.160361
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.133162
obj = -65.810461, rho = 0.165013
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.115374
obj = -72.977302, rho = 0.160152
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.931394
obj = -6.941775, rho = -0.151713
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.869607
obj = -8.125704, rho = -0.220797
nSV = 89, nBSV = 86
Total nSV = 89
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.816772
obj = -9.434984, rho = -0.174720
nSV = 83, nBSV = 80
Total nSV = 83
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.754277
obj = -10.873268, rho = -0.179257
nSV = 76, nBSV = 73
Total nSV = 76
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.682798
obj = -12.448597, rho = -0.144954
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.626200
obj = -14.148182, rho = -0.233764
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.550926
obj = -16.021394, rho = -0.195911
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.490794
obj = -18.244638, rho = -0.182068
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.440000
obj = -20.769740, rho = -0.227413
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.392713
obj = -23.646323, rho = -0.225033
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.361672
obj = -26.672330, rho = -0.271433
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.320924
obj = -29.673081, rho = -0.252469
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.282287
obj = -33.063383, rho = -0.254792
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.242277
obj = -36.833842, rho = -0.250545
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.213361
obj = -41.298767, rho = -0.251849
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.191145
obj = -46.033263, rho = -0.336170
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.167929
obj = -50.985606, rho = -0.448783
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.144500
obj = -56.295351, rho = -0.535513
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.124352
obj = -62.461264, rho = -0.619729
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.107921
obj = -69.380728, rho = -0.676353
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -6.549320, rho = 0.153618
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 85% (85/100) (classification)
Accuracy = 82.6% (826/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.819105
obj = -7.708683, rho = -0.007348
nSV = 83, nBSV = 79
Total nSV = 83
Accuracy = 91% (91/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.756187
obj = -9.001374, rho = -0.034114
nSV = 76, nBSV = 74
Total nSV = 76
Accuracy = 94% (94/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.705223
obj = -10.464848, rho = -0.051799
nSV = 72, nBSV = 69
Total nSV = 72
Accuracy = 96% (96/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.654805
obj = -12.059790, rho = -0.148369
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.587625
obj = -13.852157, rho = -0.171746
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.533698
obj = -15.893229, rho = -0.178070
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.482612
obj = -18.174432, rho = -0.248952
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.430419
obj = -20.785764, rho = -0.258929
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.383148
obj = -23.881132, rho = -0.243844
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.355944
obj = -27.327996, rho = -0.377392
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.313307
obj = -31.154834, rho = -0.366101
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.285480
obj = -35.552765, rho = -0.361359
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.254878
obj = -40.522948, rho = -0.321342
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.230592
obj = -45.721453, rho = -0.291676
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.201839
obj = -51.805250, rho = -0.270339
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.185236
obj = -58.396754, rho = -0.213573
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.161930
obj = -65.318232, rho = -0.244622
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.144365
obj = -73.194709, rho = -0.223668
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.127126
obj = -81.456460, rho = -0.173789
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.900000
obj = -7.149195, rho = -0.407514
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 85% (85/100) (classification)
Accuracy = 89.2% (892/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.877801
obj = -8.480921, rho = -0.334198
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 91% (91/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.826458
obj = -9.969451, rho = -0.272077
nSV = 84, nBSV = 80
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.777123
obj = -11.632633, rho = -0.189985
nSV = 78, nBSV = 76
Total nSV = 78
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.718075
obj = -13.437530, rho = -0.133802
nSV = 75, nBSV = 70
Total nSV = 75
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.661515
obj = -15.434598, rho = -0.129024
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.598113
obj = -17.698838, rho = -0.114861
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.535588
obj = -20.198302, rho = -0.113527
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.477515
obj = -23.133177, rho = -0.071455
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.423581
obj = -26.590097, rho = -0.081722
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.381867
obj = -30.660763, rho = -0.080332
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.346969
obj = -35.348470, rho = -0.060038
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.318912
obj = -40.783793, rho = -0.142529
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.296750
obj = -46.334852, rho = -0.088520
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.267612
obj = -52.167398, rho = -0.062375
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.237073
obj = -58.373776, rho = -0.154980
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.206230
obj = -65.549058, rho = -0.159502
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.183998
obj = -73.410942, rho = -0.238796
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.167404
obj = -81.070145, rho = -0.346885
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..............*
optimization finished, #iter = 1445
nu = 0.144440
obj = -88.601580, rho = -0.351767
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.880000
obj = -6.804484, rho = 0.087349
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 93% (93/100) (classification)
Accuracy = 90.4% (904/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.855563
obj = -8.014469, rho = -0.019522
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.810074
obj = -9.292914, rho = 0.015397
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.736039
obj = -10.700061, rho = -0.010704
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.665526
obj = -12.313612, rho = -0.046849
nSV = 69, nBSV = 66
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.622599
obj = -13.999306, rho = -0.054172
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.551752
obj = -15.781489, rho = -0.027854
nSV = 59, nBSV = 52
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.494974
obj = -17.787945, rho = -0.048435
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.434765
obj = -19.986821, rho = -0.066778
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.382838
obj = -22.475056, rho = -0.047383
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.344302
obj = -25.266813, rho = 0.031821
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.298422
obj = -28.343825, rho = 0.049103
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.258891
obj = -31.929052, rho = 0.041229
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.231587
obj = -36.137532, rho = 0.084643
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.202155
obj = -41.039407, rho = 0.086629
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.184267
obj = -46.543442, rho = 0.022443
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.162632
obj = -52.562858, rho = -0.042034
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.140237
obj = -59.787872, rho = -0.071559
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 274
nu = 0.125247
obj = -68.304354, rho = -0.151613
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.109006
obj = -78.783438, rho = -0.154845
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.840000
obj = -6.782723, rho = 0.306557
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 79% (79/100) (classification)
Accuracy = 78.3% (783/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.826177
obj = -8.082262, rho = 0.169802
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 92% (92/100) (classification)
Accuracy = 91.2% (912/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.807787
obj = -9.476401, rho = 0.088214
nSV = 82, nBSV = 80
Total nSV = 82
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.758019
obj = -10.936658, rho = 0.061469
nSV = 77, nBSV = 73
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.700000
obj = -12.499948, rho = 0.009334
nSV = 71, nBSV = 68
Total nSV = 71
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.628282
obj = -14.176415, rho = 0.046365
nSV = 66, nBSV = 60
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.564861
obj = -16.029265, rho = 0.076302
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.503088
obj = -18.007731, rho = 0.080106
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.450245
obj = -20.152956, rho = -0.010435
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.397719
obj = -22.395107, rho = -0.016496
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.343857
obj = -24.865523, rho = -0.088514
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.298869
obj = -27.684400, rho = -0.101819
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.259377
obj = -30.882062, rho = -0.150147
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.221034
obj = -34.753771, rho = -0.156543
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.197405
obj = -39.401230, rho = -0.114252
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.173990
obj = -44.515838, rho = -0.073393
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.157150
obj = -50.313970, rho = -0.146022
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.138190
obj = -56.625409, rho = -0.185370
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.122458
obj = -63.737062, rho = -0.195090
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.111154
obj = -71.724785, rho = -0.311154
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.865553
obj = -6.207968, rho = -0.221161
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 97% (97/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.800000
obj = -7.186536, rho = -0.231327
nSV = 80, nBSV = 80
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.729055
obj = -8.234077, rho = -0.211820
nSV = 75, nBSV = 72
Total nSV = 75
Accuracy = 97% (97/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.667240
obj = -9.392219, rho = -0.203675
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.608206
obj = -10.620707, rho = -0.218205
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.539051
obj = -11.984879, rho = -0.270350
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.469701
obj = -13.534662, rho = -0.250670
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.421196
obj = -15.321001, rho = -0.190899
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.375752
obj = -17.320431, rho = -0.164077
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.336332
obj = -19.513330, rho = -0.246101
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.293662
obj = -21.896323, rho = -0.232022
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.260000
obj = -24.673960, rho = -0.210730
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.230984
obj = -27.619782, rho = -0.174757
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.203366
obj = -30.948413, rho = -0.155186
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.179108
obj = -34.676981, rho = -0.083923
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.155545
obj = -38.903901, rho = -0.103416
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.140052
obj = -43.647498, rho = -0.087966
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.123787
obj = -48.574227, rho = 0.017448
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.108178
obj = -54.068971, rho = 0.128717
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.094516
obj = -59.804597, rho = 0.183130
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.940000
obj = -6.983062, rho = -0.224876
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.900000
obj = -8.101395, rho = -0.102300
nSV = 91, nBSV = 89
Total nSV = 91
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.837344
obj = -9.280127, rho = -0.033530
nSV = 85, nBSV = 81
Total nSV = 85
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.749449
obj = -10.555119, rho = -0.028133
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.676551
obj = -11.975617, rho = -0.023836
nSV = 69, nBSV = 64
Total nSV = 69
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.600432
obj = -13.572438, rho = 0.001482
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.539835
obj = -15.305503, rho = -0.085290
nSV = 58, nBSV = 51
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.476455
obj = -17.231977, rho = -0.090458
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.432776
obj = -19.319340, rho = -0.156592
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.374285
obj = -21.564853, rho = -0.152658
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.329586
obj = -24.048102, rho = -0.118837
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.289634
obj = -26.785158, rho = -0.122211
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.251164
obj = -29.939594, rho = -0.153504
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.218412
obj = -33.596384, rho = -0.177335
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.189058
obj = -37.870389, rho = -0.203223
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.170981
obj = -42.792153, rho = -0.271664
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.156010
obj = -47.804585, rho = -0.291117
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.141507
obj = -52.405143, rho = -0.294564
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.121820
obj = -56.580296, rho = -0.283845
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 282
nu = 0.103563
obj = -60.852698, rho = -0.281740
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.900000
obj = -7.086155, rho = -0.346086
nSV = 90, nBSV = 90
Total nSV = 90
Accuracy = 90% (90/100) (classification)
Accuracy = 89.8% (898/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.880000
obj = -8.370171, rho = -0.231251
nSV = 88, nBSV = 88
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.831549
obj = -9.746677, rho = -0.166218
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.775273
obj = -11.229971, rho = -0.105153
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.703873
obj = -12.846493, rho = -0.092431
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.629325
obj = -14.684118, rho = -0.154935
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.570041
obj = -16.842024, rho = -0.170867
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.515528
obj = -19.227388, rho = -0.125340
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.460966
obj = -21.852496, rho = -0.149281
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.411621
obj = -24.855652, rho = -0.180191
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.369829
obj = -28.314394, rho = -0.170441
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.331960
obj = -32.056339, rho = -0.113784
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.295983
obj = -36.380511, rho = -0.081496
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.259332
obj = -41.317365, rho = -0.115016
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 167
nu = 0.229977
obj = -47.023648, rho = -0.081613
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.208634
obj = -53.408821, rho = 0.013365
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.184770
obj = -60.695590, rho = 0.071960
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.161557
obj = -69.266758, rho = 0.101758
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.142963
obj = -79.559790, rho = 0.125928
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.126747
obj = -92.093619, rho = 0.108741
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.852727
obj = -6.281948, rho = -0.329961
nSV = 88, nBSV = 84
Total nSV = 88
Accuracy = 97% (97/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.781381
obj = -7.335054, rho = -0.331828
nSV = 80, nBSV = 76
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 93.6% (936/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.728613
obj = -8.550170, rho = -0.265452
nSV = 74, nBSV = 72
Total nSV = 74
Accuracy = 98% (98/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.666380
obj = -9.924683, rho = -0.252628
nSV = 68, nBSV = 63
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.614117
obj = -11.489441, rho = -0.195291
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.564061
obj = -13.201254, rho = -0.151031
nSV = 58, nBSV = 56
Total nSV = 58
Accuracy = 96% (96/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.513965
obj = -15.021744, rho = -0.123631
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.460878
obj = -17.061030, rho = -0.140729
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.424238
obj = -19.286252, rho = -0.110867
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.377758
obj = -21.591394, rho = -0.149195
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.333119
obj = -24.031787, rho = -0.142193
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.289217
obj = -26.777027, rho = -0.161831
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.251245
obj = -29.857129, rho = -0.152278
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.215335
obj = -33.471453, rho = -0.144691
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.189107
obj = -37.793732, rho = -0.186773
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.165727
obj = -42.953835, rho = -0.236532
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.151453
obj = -48.808311, rho = -0.380157
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.138486
obj = -54.887526, rho = -0.463870
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.125254
obj = -60.423061, rho = -0.453348
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.107975
obj = -66.197299, rho = -0.502695
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.906112
obj = -6.832295, rho = -0.169236
nSV = 92, nBSV = 90
Total nSV = 92
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.847331
obj = -8.007942, rho = -0.216464
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.799685
obj = -9.337553, rho = -0.317445
nSV = 80, nBSV = 78
Total nSV = 80
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.727729
obj = -10.816826, rho = -0.291106
nSV = 75, nBSV = 71
Total nSV = 75
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.672594
obj = -12.515320, rho = -0.294624
nSV = 68, nBSV = 65
Total nSV = 68
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.613629
obj = -14.420006, rho = -0.285421
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.554645
obj = -16.569417, rho = -0.295746
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.509193
obj = -18.931933, rho = -0.277230
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.460000
obj = -21.501979, rho = -0.339428
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.406406
obj = -24.340435, rho = -0.372879
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.365766
obj = -27.576237, rho = -0.376894
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.329611
obj = -30.998075, rho = -0.431826
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.288760
obj = -34.836355, rho = -0.416406
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.252155
obj = -39.226761, rho = -0.445311
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.232378
obj = -44.029935, rho = -0.394131
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.202336
obj = -48.738974, rho = -0.395627
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.175397
obj = -54.181338, rho = -0.364816
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.152710
obj = -60.497338, rho = -0.410339
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.132818
obj = -67.514619, rho = -0.412059
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 252
nu = 0.115213
obj = -75.550543, rho = -0.411291
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.840000
obj = -6.780943, rho = 0.343888
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 79% (79/100) (classification)
Accuracy = 74.7% (747/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.840000
obj = -8.074923, rho = 0.163932
nSV = 85, nBSV = 83
Total nSV = 85
Accuracy = 94% (94/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820000
obj = -9.438613, rho = 0.091273
nSV = 83, nBSV = 81
Total nSV = 83
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.760000
obj = -10.835626, rho = 0.050212
nSV = 78, nBSV = 74
Total nSV = 78
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.686856
obj = -12.327088, rho = -0.023000
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.620721
obj = -13.976132, rho = 0.028652
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.550334
obj = -15.776254, rho = 0.028370
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.490999
obj = -17.879441, rho = -0.009675
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.433558
obj = -20.200217, rho = -0.014817
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.380128
obj = -22.941621, rho = -0.006530
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.342022
obj = -26.018753, rho = 0.016590
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.310944
obj = -29.319740, rho = 0.055229
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.273343
obj = -32.856720, rho = 0.044725
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.243309
obj = -36.803934, rho = 0.028254
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.211236
obj = -41.156309, rho = 0.001468
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.186126
obj = -46.207830, rho = -0.021164
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.163033
obj = -51.770860, rho = 0.044919
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.143951
obj = -58.113091, rho = 0.164966
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.124954
obj = -65.417553, rho = 0.197109
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.110008
obj = -74.025791, rho = 0.216886
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.877279
obj = -6.401824, rho = -0.431193
nSV = 89, nBSV = 85
Total nSV = 89
Accuracy = 94% (94/100) (classification)
Accuracy = 92.9% (929/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.811334
obj = -7.448146, rho = -0.399464
nSV = 83, nBSV = 78
Total nSV = 83
Accuracy = 96% (96/100) (classification)
Accuracy = 94.2% (942/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.752528
obj = -8.603125, rho = -0.357261
nSV = 77, nBSV = 74
Total nSV = 77
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.682923
obj = -9.871427, rho = -0.336183
nSV = 71, nBSV = 67
Total nSV = 71
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.622097
obj = -11.309798, rho = -0.373092
nSV = 64, nBSV = 61
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.567988
obj = -12.886506, rho = -0.327793
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.508079
obj = -14.593036, rho = -0.341654
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.457109
obj = -16.492973, rho = -0.288224
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.398773
obj = -18.648345, rho = -0.285196
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.362143
obj = -21.017247, rho = -0.264929
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.315564
obj = -23.575059, rho = -0.248974
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.274818
obj = -26.605653, rho = -0.282953
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.241350
obj = -30.278315, rho = -0.288251
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.217270
obj = -34.459784, rho = -0.246085
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.194165
obj = -39.144153, rho = -0.238805
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.175487
obj = -44.348394, rho = -0.262306
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.156243
obj = -49.966210, rho = -0.247160
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.140404
obj = -55.887747, rho = -0.237080
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.124181
obj = -62.411289, rho = -0.232430
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.108806
obj = -69.379297, rho = -0.173222
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.940000
obj = -7.199667, rho = -0.192515
nSV = 94, nBSV = 94
Total nSV = 94
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.889089
obj = -8.500163, rho = -0.183566
nSV = 90, nBSV = 88
Total nSV = 90
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.847363
obj = -9.926530, rho = -0.130580
nSV = 86, nBSV = 84
Total nSV = 86
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.789609
obj = -11.457193, rho = -0.134707
nSV = 81, nBSV = 78
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.717706
obj = -13.106302, rho = -0.176592
nSV = 72, nBSV = 70
Total nSV = 72
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.640000
obj = -15.003465, rho = -0.166127
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.577246
obj = -17.178104, rho = -0.128615
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.522571
obj = -19.705163, rho = -0.096138
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.470524
obj = -22.490185, rho = -0.067676
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.430664
obj = -25.549511, rho = 0.020247
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.386567
obj = -28.810804, rho = -0.033204
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.340811
obj = -32.336837, rho = -0.027148
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.296734
obj = -36.476301, rho = -0.052677
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.267395
obj = -41.130480, rho = -0.079663
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.229872
obj = -46.508923, rho = -0.064821
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.205837
obj = -52.911702, rho = -0.031958
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.184553
obj = -59.933443, rho = 0.009113
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.161732
obj = -67.965352, rho = 0.015564
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.142117
obj = -77.630422, rho = -0.001434
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.124636
obj = -89.583455, rho = -0.000127
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.900000
obj = -7.148791, rho = 0.358767
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 84% (84/100) (classification)
Accuracy = 82.6% (826/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.900000
obj = -8.462526, rho = 0.182425
nSV = 92, nBSV = 88
Total nSV = 92
Accuracy = 95% (95/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.842858
obj = -9.822293, rho = 0.120058
nSV = 87, nBSV = 81
Total nSV = 87
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.779992
obj = -11.332108, rho = 0.054280
nSV = 79, nBSV = 75
Total nSV = 79
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.714681
obj = -13.011442, rho = 0.056032
nSV = 73, nBSV = 68
Total nSV = 73
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.647493
obj = -14.838623, rho = 0.014470
nSV = 67, nBSV = 61
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.589941
obj = -16.872416, rho = 0.007629
nSV = 60, nBSV = 57
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.533971
obj = -18.994138, rho = 0.011632
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.462560
obj = -21.299716, rho = 0.021932
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.408300
obj = -24.000907, rho = 0.018543
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.365330
obj = -27.019194, rho = 0.037529
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.312724
obj = -30.510811, rho = 0.051116
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.280455
obj = -34.615948, rho = 0.086642
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.250718
obj = -39.179380, rho = 0.043368
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.220582
obj = -44.371329, rho = 0.013212
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.190744
obj = -50.682766, rho = 0.022020
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.172120
obj = -58.279919, rho = 0.000939
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.154365
obj = -67.034682, rho = -0.040111
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*....*
optimization finished, #iter = 434
nu = 0.138224
obj = -77.282617, rho = -0.095234
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*.*
optimization finished, #iter = 268
nu = 0.125148
obj = -89.494422, rho = -0.140007
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -6.935695, rho = 0.403929
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 76% (76/100) (classification)
Accuracy = 75.1% (751/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.860000
obj = -8.256305, rho = 0.240442
nSV = 87, nBSV = 85
Total nSV = 87
Accuracy = 90% (90/100) (classification)
Accuracy = 91.1% (911/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.838148
obj = -9.610297, rho = 0.101942
nSV = 84, nBSV = 82
Total nSV = 84
Accuracy = 95% (95/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.759776
obj = -11.084798, rho = 0.073286
nSV = 78, nBSV = 73
Total nSV = 78
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.684964
obj = -12.762603, rho = 0.018385
nSV = 71, nBSV = 66
Total nSV = 71
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.637557
obj = -14.649828, rho = -0.075761
nSV = 66, nBSV = 61
Total nSV = 66
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.572572
obj = -16.679666, rho = -0.116486
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.504999
obj = -18.979308, rho = -0.116865
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.451155
obj = -21.725715, rho = -0.120638
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.401861
obj = -24.960587, rho = -0.122456
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.370586
obj = -28.573855, rho = -0.202681
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.326761
obj = -32.557217, rho = -0.189734
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.296165
obj = -37.089797, rho = -0.235159
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.265585
obj = -42.142046, rho = -0.220313
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.234513
obj = -48.049845, rho = -0.235663
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.208677
obj = -55.044862, rho = -0.210418
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.193114
obj = -62.712644, rho = -0.145527
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.167102
obj = -71.348624, rho = -0.133765
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.154853
obj = -81.451565, rho = -0.032075
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.136837
obj = -92.027273, rho = 0.052964
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.924736
obj = -6.995699, rho = -0.190349
nSV = 94, nBSV = 92
Total nSV = 94
Accuracy = 95% (95/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.872315
obj = -8.211051, rho = -0.169046
nSV = 88, nBSV = 86
Total nSV = 88
Accuracy = 96% (96/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.820800
obj = -9.553670, rho = -0.076553
nSV = 84, nBSV = 81
Total nSV = 84
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.744753
obj = -11.050807, rho = -0.092503
nSV = 79, nBSV = 73
Total nSV = 79
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.690548
obj = -12.746167, rho = -0.114741
nSV = 70, nBSV = 67
Total nSV = 70
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.618700
obj = -14.642504, rho = -0.102643
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.556581
obj = -16.869188, rho = -0.120317
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.505925
obj = -19.432431, rho = -0.182999
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.453363
obj = -22.395555, rho = -0.176609
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.420000
obj = -25.827300, rho = -0.148459
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.383752
obj = -29.321371, rho = -0.099611
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.341926
obj = -33.389768, rho = -0.063013
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.302124
obj = -37.996109, rho = -0.018603
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.266708
obj = -43.376685, rho = 0.003910
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.241096
obj = -49.617600, rho = 0.016108
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.217917
obj = -56.784815, rho = -0.021229
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.194192
obj = -64.682223, rho = -0.001792
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.174914
obj = -73.995873, rho = 0.000406
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*..*
optimization finished, #iter = 358
nu = 0.156340
obj = -84.135737, rho = 0.017385
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.138062
obj = -96.329004, rho = 0.073492
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.929376
obj = -6.786938, rho = -0.031914
nSV = 95, nBSV = 92
Total nSV = 95
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.861819
obj = -7.858622, rho = -0.095588
nSV = 88, nBSV = 83
Total nSV = 88
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.793331
obj = -9.083553, rho = -0.104737
nSV = 81, nBSV = 77
Total nSV = 81
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.715556
obj = -10.444893, rho = -0.078577
nSV = 74, nBSV = 70
Total nSV = 74
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.648918
obj = -12.031422, rho = -0.069169
nSV = 67, nBSV = 64
Total nSV = 67
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.594705
obj = -13.797172, rho = -0.087574
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.546089
obj = -15.703794, rho = -0.099394
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.486764
obj = -17.720571, rho = -0.113900
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.426860
obj = -20.016830, rho = -0.124342
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.379672
obj = -22.661684, rho = -0.210838
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.340210
obj = -25.629244, rho = -0.157868
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.304604
obj = -28.919741, rho = -0.205873
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.274762
obj = -32.387872, rho = -0.183864
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.242217
obj = -36.028185, rho = -0.187181
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.209159
obj = -40.056199, rho = -0.181068
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.184030
obj = -44.538100, rho = -0.215136
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.161746
obj = -49.339595, rho = -0.276097
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 179
nu = 0.147825
obj = -53.930005, rho = -0.180144
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 252
nu = 0.126204
obj = -58.118193, rho = -0.125529
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 208
nu = 0.104928
obj = -62.568967, rho = -0.117935
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.960000
obj = -7.155308, rho = 0.146796
nSV = 96, nBSV = 96
Total nSV = 96
Accuracy = 96% (96/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.909103
obj = -8.319553, rho = 0.077516
nSV = 93, nBSV = 88
Total nSV = 93
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.836923
obj = -9.594955, rho = 0.038116
nSV = 85, nBSV = 82
Total nSV = 85
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.766155
obj = -11.021154, rho = 0.091607
nSV = 79, nBSV = 76
Total nSV = 79
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.694504
obj = -12.594021, rho = 0.102208
nSV = 72, nBSV = 68
Total nSV = 72
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.627275
obj = -14.310057, rho = 0.132211
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.561575
obj = -16.273603, rho = 0.165616
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.505713
obj = -18.434708, rho = 0.106839
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.450974
obj = -20.853456, rho = 0.094055
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.395216
obj = -23.575061, rho = 0.084331
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.355146
obj = -26.565685, rho = 0.145808
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.307961
obj = -30.016449, rho = 0.191646
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.269821
obj = -34.207098, rho = 0.229346
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.242351
obj = -39.179675, rho = 0.228086
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.218562
obj = -44.739507, rho = 0.189904
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.197914
obj = -50.838703, rho = 0.192452
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.178643
obj = -57.262714, rho = 0.275496
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.161827
obj = -64.394396, rho = 0.264536
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.141437
obj = -71.823595, rho = 0.212346
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.124272
obj = -80.160252, rho = 0.159786
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
No description has been provided for this image
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt


def data(N,sigma):   
    w = np.ones(10)/np.sqrt(10)   
    w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)   
    w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)   
    x = np.zeros((4,10))   
    x[1,:] = x[0,:] + sigma*w1   
    x[2,:] = x[0,:] + sigma*w2   
    x[3,:] = x[2,:] + sigma*w1   
    X1 = x + sigma*matlib.repmat(w,4,1)/2   
    X2 = x - sigma*matlib.repmat(w,4,1)/2   
    X1 = matlib.repmat(X1,2*N,1)   
    X2 = matlib.repmat(X2,2*N,1)   
    X = np.concatenate((X1, X2), axis=0)   
    Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)   
    Z = np.random.permutation(16*N)   
    Z = Z[:N]   
    X = X[Z,:]   
    X = X + 0.2*sigma*np.random.randn(N,10)   
    Y = Y[Z]

    return X, Y

# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-1, 1, 20)  # Logarithmically spaced values between 0.01 and 10

# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 1

# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):

    # Generate data
    X_train, y_train = data(100,sigma)
    X_test, y_test = data(1000, sigma)

    for lam in lambda_values:
        
        # Train SVM
        svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
        param = svm_parameter(f'-t 0 -c {lam}')
        model = svm_train(svm_problem_setup, param)
        
        # Predict on training and test data
        i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
        i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
        
        # Calculate errors
        training_errors.append(100 - train_accuracy[0])  # Convert to error percentage
        test_errors.append(100 - test_accuracy[0])  # Convert to error percentage

# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)

avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)

lambda_values_log = np.log10(lambda_values)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')

plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.1,10)@ σ = 1')
plt.legend()
plt.show()
*
optimization finished, #iter = 41
nu = 0.524674
obj = -3.426639, rho = 0.054033
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.456950
obj = -3.849325, rho = 0.089089
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.407930
obj = -4.317322, rho = 0.087179
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.360909
obj = -4.830806, rho = 0.080314
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.317275
obj = -5.406955, rho = 0.141655
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.275960
obj = -6.042330, rho = 0.192253
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.238280
obj = -6.786037, rho = 0.233015
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.210946
obj = -7.657470, rho = 0.273550
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.189372
obj = -8.584299, rho = 0.242646
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.169735
obj = -9.612962, rho = 0.354699
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*..*
optimization finished, #iter = 214
nu = 0.148726
obj = -10.629356, rho = 0.398824
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.129161
obj = -11.793983, rho = 0.420145
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.119714
obj = -12.858513, rho = 0.558814
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.100362
obj = -13.801609, rho = 0.593642
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.085470
obj = -14.791812, rho = 0.627258
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.073765
obj = -15.729160, rho = 0.719111
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.060541
obj = -16.592606, rho = 0.735506
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.049925
obj = -17.646933, rho = 0.744050
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.044589
obj = -18.514225, rho = 0.835648
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.036532
obj = -18.880787, rho = 0.878620
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.633402
obj = -4.360823, rho = -0.165353
nSV = 64, nBSV = 62
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.564777
obj = -4.970838, rho = -0.143532
nSV = 61, nBSV = 53
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.509844
obj = -5.682698, rho = -0.241055
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.456039
obj = -6.473726, rho = -0.226976
nSV = 49, nBSV = 41
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.411579
obj = -7.366888, rho = -0.180442
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.364257
obj = -8.381903, rho = -0.170899
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.318032
obj = -9.597034, rho = -0.166548
nSV = 38, nBSV = 28
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.285009
obj = -11.091985, rho = -0.189695
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.259607
obj = -12.779173, rho = -0.255786
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.229352
obj = -14.806849, rho = -0.226138
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.209512
obj = -17.254770, rho = -0.198102
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.198004
obj = -19.996378, rho = -0.038794
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.181564
obj = -22.929080, rho = 0.036565
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.164780
obj = -26.180854, rho = 0.068090
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*....*
optimization finished, #iter = 606
nu = 0.145199
obj = -29.746157, rho = 0.081874
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.129084
obj = -34.108557, rho = 0.062484
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.115398
obj = -39.205744, rho = 0.017936
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.103762
obj = -45.010564, rho = 0.035588
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 435
nu = 0.093642
obj = -51.917830, rho = 0.168192
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*..*
optimization finished, #iter = 470
nu = 0.082396
obj = -60.145401, rho = 0.167415
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.598373
obj = -4.032125, rho = -0.092021
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.536987
obj = -4.559551, rho = -0.153330
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.486807
obj = -5.126735, rho = -0.142706
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.423815
obj = -5.725633, rho = -0.131543
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.377235
obj = -6.397743, rho = -0.122084
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.326639
obj = -7.151575, rho = -0.103612
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.286058
obj = -8.015422, rho = -0.120340
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.249183
obj = -9.000918, rho = -0.122002
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.222434
obj = -10.115382, rho = -0.135626
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.194896
obj = -11.326522, rho = -0.151782
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.168687
obj = -12.764837, rho = -0.150504
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 298
nu = 0.145566
obj = -14.525523, rho = -0.148360
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.127867
obj = -16.682366, rho = -0.177614
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.113119
obj = -19.383068, rho = -0.191197
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.106589
obj = -22.544858, rho = -0.164963
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.097991
obj = -25.879296, rho = -0.135686
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.085309
obj = -29.871063, rho = -0.121708
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.078612
obj = -34.725907, rho = -0.095499
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.072496
obj = -40.182843, rho = -0.115764
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.068479
obj = -45.738429, rho = -0.296634
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.566794
obj = -3.789309, rho = -0.192724
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.507280
obj = -4.272957, rho = -0.202052
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.444653
obj = -4.820088, rho = -0.273180
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.392741
obj = -5.439516, rho = -0.358957
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.354305
obj = -6.138155, rho = -0.367227
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.317615
obj = -6.880720, rho = -0.187618
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.274601
obj = -7.701378, rho = -0.216300
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.243142
obj = -8.617110, rho = -0.253439
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.213671
obj = -9.651309, rho = -0.355843
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.182592
obj = -10.857688, rho = -0.350807
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.162059
obj = -12.329893, rho = -0.346352
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.141556
obj = -13.995748, rho = -0.344140
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.128008
obj = -15.968067, rho = -0.396838
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.116226
obj = -18.108468, rho = -0.445162
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.103635
obj = -20.416922, rho = -0.375920
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 273
nu = 0.089952
obj = -23.025799, rho = -0.343108
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.079335
obj = -26.146787, rho = -0.319371
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.071995
obj = -29.693867, rho = -0.243584
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.065659
obj = -33.399576, rho = -0.200385
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.059795
obj = -36.875643, rho = -0.122925
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.575878
obj = -4.030179, rho = -0.117439
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.525038
obj = -4.624628, rho = -0.051531
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.474760
obj = -5.275695, rho = -0.113356
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.425649
obj = -6.014769, rho = -0.158107
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.381637
obj = -6.817999, rho = -0.138205
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.338720
obj = -7.754752, rho = -0.091198
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.307727
obj = -8.810544, rho = -0.148431
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.279381
obj = -9.912478, rho = -0.134728
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.242426
obj = -11.095602, rho = -0.140498
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.212308
obj = -12.483249, rho = -0.132954
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.185480
obj = -14.099271, rho = -0.125549
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.166311
obj = -15.963932, rho = -0.121195
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.147127
obj = -17.967265, rho = -0.137190
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*...*
optimization finished, #iter = 461
nu = 0.127471
obj = -20.387112, rho = -0.098693
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 266
nu = 0.113791
obj = -23.240545, rho = -0.121412
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.102201
obj = -26.475022, rho = -0.128762
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.092700
obj = -30.038788, rho = -0.106127
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.081975
obj = -34.104281, rho = -0.097644
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.074390
obj = -38.344603, rho = -0.133895
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.064609
obj = -43.235222, rho = -0.104981
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.537970
obj = -3.735219, rho = -0.361334
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.480000
obj = -4.289577, rho = -0.362031
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.439936
obj = -4.905142, rho = -0.293943
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.393548
obj = -5.604305, rho = -0.276642
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.350697
obj = -6.406144, rho = -0.283145
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.316440
obj = -7.302437, rho = -0.376562
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.287622
obj = -8.309957, rho = -0.397898
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.258115
obj = -9.379955, rho = -0.401159
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.231509
obj = -10.566708, rho = -0.400909
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.206735
obj = -11.829984, rho = -0.425726
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.184273
obj = -13.141870, rho = -0.441441
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.161060
obj = -14.482036, rho = -0.426516
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.139557
obj = -15.986074, rho = -0.431313
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.118947
obj = -17.657866, rho = -0.446923
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.101214
obj = -19.672253, rho = -0.480158
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.093790
obj = -21.900556, rho = -0.545001
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.081872
obj = -23.832461, rho = -0.718284
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.070905
obj = -25.797018, rho = -0.930667
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.063833
obj = -27.447009, rho = -1.232219
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.052576
obj = -28.431062, rho = -1.371294
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.510124
obj = -3.460520, rho = -0.158433
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.458232
obj = -3.926896, rho = -0.206117
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.406245
obj = -4.444559, rho = -0.251948
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.363767
obj = -5.011690, rho = -0.270522
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.322376
obj = -5.643622, rho = -0.330951
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.282803
obj = -6.368024, rho = -0.399451
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.250664
obj = -7.194812, rho = -0.378748
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.222664
obj = -8.124402, rho = -0.346695
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.200000
obj = -9.199696, rho = -0.291748
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.175791
obj = -10.346148, rho = -0.267583
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.157823
obj = -11.606999, rho = -0.301152
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.137648
obj = -13.002738, rho = -0.316721
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.121924
obj = -14.630057, rho = -0.297584
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.106996
obj = -16.434666, rho = -0.228643
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.095566
obj = -18.414425, rho = -0.153021
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 287
nu = 0.085782
obj = -20.389426, rho = -0.161995
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.072363
obj = -22.643929, rho = -0.184102
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.062785
obj = -25.335368, rho = -0.191745
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 350
nu = 0.054256
obj = -28.521349, rho = -0.205003
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 338
nu = 0.046650
obj = -32.451961, rho = -0.208744
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.521721
obj = -3.518682, rho = -0.211027
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.470358
obj = -3.966321, rho = -0.189445
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.416749
obj = -4.466159, rho = -0.144464
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.368761
obj = -5.008778, rho = -0.132146
nSV = 40, nBSV = 30
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.320080
obj = -5.639817, rho = -0.144975
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.289020
obj = -6.381834, rho = -0.099651
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.257277
obj = -7.153107, rho = -0.055592
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.228101
obj = -7.973186, rho = 0.030352
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.200846
obj = -8.837806, rho = 0.009743
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.174889
obj = -9.816861, rho = 0.010848
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.154826
obj = -10.796379, rho = 0.100338
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.132128
obj = -11.880092, rho = 0.071298
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.120463
obj = -12.973109, rho = -0.013815
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.101993
obj = -13.970643, rho = -0.042453
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.084910
obj = -15.051270, rho = -0.043099
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.072504
obj = -16.296105, rho = -0.075885
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.062400
obj = -17.538149, rho = -0.155733
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.054004
obj = -18.656309, rho = -0.275186
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*.*
optimization finished, #iter = 394
nu = 0.044720
obj = -19.655164, rho = -0.349115
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...........*..*
optimization finished, #iter = 1371
nu = 0.036669
obj = -20.710429, rho = -0.388398
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.561713
obj = -3.723660, rho = -0.187662
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.497612
obj = -4.197891, rho = -0.258976
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.447002
obj = -4.706681, rho = -0.217578
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.394534
obj = -5.249465, rho = -0.238001
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.350568
obj = -5.842852, rho = -0.233623
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.302765
obj = -6.482313, rho = -0.223827
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.267676
obj = -7.153977, rho = -0.183113
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.232470
obj = -7.884295, rho = -0.214153
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.196900
obj = -8.680628, rho = -0.236592
nSV = 26, nBSV = 15
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.167765
obj = -9.667779, rho = -0.217135
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.146024
obj = -10.848209, rho = -0.246008
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.128041
obj = -12.205752, rho = -0.276023
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.115270
obj = -13.690133, rho = -0.319183
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 261
nu = 0.101321
obj = -15.221109, rho = -0.340112
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.086254
obj = -17.064210, rho = -0.353862
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.074360
obj = -19.333131, rho = -0.365922
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.067216
obj = -22.073035, rho = -0.475954
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.058283
obj = -25.246917, rho = -0.461754
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.051413
obj = -29.190959, rho = -0.435765
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.046904
obj = -33.964500, rho = -0.398524
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.586266
obj = -3.830976, rho = -0.189876
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.521502
obj = -4.279663, rho = -0.181914
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.464836
obj = -4.727898, rho = -0.246097
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.402976
obj = -5.201486, rho = -0.297189
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.344290
obj = -5.744532, rho = -0.292096
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.297088
obj = -6.374794, rho = -0.277629
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.262107
obj = -7.066334, rho = -0.385090
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.225587
obj = -7.842517, rho = -0.401724
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.199664
obj = -8.650867, rho = -0.462280
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.170355
obj = -9.540720, rho = -0.487140
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.149527
obj = -10.509269, rho = -0.584479
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.128067
obj = -11.579584, rho = -0.528775
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.109093
obj = -12.857949, rho = -0.537313
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.098765
obj = -14.227360, rho = -0.706868
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.091462
obj = -15.270496, rho = -0.796378
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 210
nu = 0.075527
obj = -16.079444, rho = -0.843790
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.063171
obj = -16.921187, rho = -0.877818
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.053327
obj = -17.639049, rho = -1.002101
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.044910
obj = -18.037050, rho = -1.035908
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.036124
obj = -18.064616, rho = -1.053589
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.482696
obj = -3.249242, rho = -0.080972
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.433539
obj = -3.674639, rho = -0.043805
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.380318
obj = -4.146110, rho = -0.059503
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.340000
obj = -4.693275, rho = -0.041651
nSV = 35, nBSV = 32
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.300718
obj = -5.311439, rho = -0.004149
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.265048
obj = -6.003667, rho = 0.042494
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.233646
obj = -6.829234, rho = 0.073272
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.204146
obj = -7.812170, rho = 0.105565
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.184917
obj = -8.991004, rho = 0.105567
nSV = 20, nBSV = 17
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.167956
obj = -10.281040, rho = 0.035187
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.153095
obj = -11.736873, rho = 0.141197
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.137421
obj = -13.311555, rho = 0.250557
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.122412
obj = -15.060159, rho = 0.374005
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.111918
obj = -16.978900, rho = 0.527250
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.099777
obj = -18.941392, rho = 0.610899
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.088430
obj = -20.895442, rho = 0.647456
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.077921
obj = -22.899415, rho = 0.749236
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.068693
obj = -24.865295, rho = 0.876593
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*...*
optimization finished, #iter = 443
nu = 0.060414
obj = -26.395875, rho = 1.007737
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*..*
optimization finished, #iter = 474
nu = 0.051556
obj = -27.507338, rho = 1.041088
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.556339
obj = -3.735080, rho = -0.252920
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.490910
obj = -4.228337, rho = -0.249165
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.440000
obj = -4.790140, rho = -0.263084
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.391977
obj = -5.428069, rho = -0.283223
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.347966
obj = -6.130474, rho = -0.274531
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.309800
obj = -6.902157, rho = -0.288083
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.278141
obj = -7.752995, rho = -0.301656
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*..*
optimization finished, #iter = 228
nu = 0.240954
obj = -8.680168, rho = -0.298583
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.211821
obj = -9.778135, rho = -0.318076
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.186807
obj = -10.993188, rho = -0.347483
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.166579
obj = -12.400472, rho = -0.298970
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.147104
obj = -13.890251, rho = -0.223916
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.129303
obj = -15.627556, rho = -0.210266
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.115710
obj = -17.520788, rho = -0.162968
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.104615
obj = -19.331630, rho = -0.065729
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.091049
obj = -21.275875, rho = -0.064203
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.083837
obj = -22.902507, rho = -0.088198
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.073182
obj = -23.992744, rho = -0.042683
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.061576
obj = -24.162412, rho = -0.031387
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.048322
obj = -24.162412, rho = -0.031387
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.571414
obj = -3.956764, rho = -0.078597
nSV = 60, nBSV = 52
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.511820
obj = -4.527600, rho = -0.035943
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.460282
obj = -5.186603, rho = -0.016345
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.411629
obj = -5.934340, rho = 0.066941
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.371331
obj = -6.786227, rho = 0.061695
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.331579
obj = -7.774679, rho = 0.126535
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.299993
obj = -8.896941, rho = 0.156147
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.270896
obj = -10.139447, rho = 0.123108
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.241791
obj = -11.582595, rho = 0.112959
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.215464
obj = -13.209649, rho = 0.192115
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.191248
obj = -15.156014, rho = 0.160453
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.177094
obj = -17.378405, rho = 0.035688
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.162820
obj = -19.649236, rho = 0.068646
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.142595
obj = -22.113131, rho = 0.069392
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.131814
obj = -24.826251, rho = 0.059653
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.116576
obj = -27.201010, rho = -0.025524
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.100031
obj = -29.836906, rho = -0.011843
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*..*
optimization finished, #iter = 559
nu = 0.086042
obj = -32.667236, rho = 0.059437
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*.*
optimization finished, #iter = 474
nu = 0.074294
obj = -35.878135, rho = 0.096641
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 365
nu = 0.065896
obj = -39.002975, rho = 0.224457
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.571436
obj = -3.852845, rho = -0.029186
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.503579
obj = -4.375192, rho = -0.051331
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.456119
obj = -4.962754, rho = -0.080345
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.402311
obj = -5.612288, rho = -0.096946
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.356986
obj = -6.360708, rho = -0.074943
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.314014
obj = -7.221383, rho = -0.119578
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.282377
obj = -8.221105, rho = -0.163225
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.251566
obj = -9.342770, rho = -0.194098
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.227287
obj = -10.584043, rho = -0.173616
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.199137
obj = -12.001682, rho = -0.169821
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.177082
obj = -13.666647, rho = -0.113665
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.159287
obj = -15.523403, rho = -0.061452
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.148106
obj = -17.443284, rho = -0.253001
nSV = 17, nBSV = 13
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.129980
obj = -19.402611, rho = -0.214165
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.118999
obj = -21.412907, rho = -0.087743
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.106263
obj = -22.966340, rho = -0.042066
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.091726
obj = -24.136259, rho = -0.048349
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.076318
obj = -24.889978, rho = -0.014587
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.061324
obj = -25.642946, rho = -0.007399
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.050290
obj = -26.141843, rho = -0.035933
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.591900
obj = -4.019346, rho = -0.074871
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.535355
obj = -4.551700, rho = -0.173094
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.471642
obj = -5.146496, rho = -0.189977
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.423468
obj = -5.792129, rho = -0.233424
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.374255
obj = -6.520496, rho = -0.225404
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.328185
obj = -7.333253, rho = -0.233066
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.292211
obj = -8.265603, rho = -0.197860
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.254108
obj = -9.337681, rho = -0.229209
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.230533
obj = -10.563070, rho = -0.273925
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.209702
obj = -11.813850, rho = -0.163491
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*..*
optimization finished, #iter = 334
nu = 0.189240
obj = -12.927778, rho = -0.266326
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*..*
optimization finished, #iter = 213
nu = 0.162339
obj = -14.035096, rho = -0.263160
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.140654
obj = -15.190975, rho = -0.330936
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.118409
obj = -16.394422, rho = -0.300660
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.101026
obj = -17.594645, rho = -0.253241
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.090385
obj = -18.628902, rho = -0.254357
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 169
nu = 0.074954
obj = -19.264315, rho = -0.205588
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.060940
obj = -19.759575, rho = -0.166450
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.049635
obj = -20.195146, rho = -0.092645
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 356
nu = 0.040221
obj = -20.428789, rho = 0.027311
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.571366
obj = -3.860609, rho = -0.040854
nSV = 61, nBSV = 53
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.502266
obj = -4.395034, rho = -0.080784
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.443162
obj = -5.026150, rho = -0.104304
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.399774
obj = -5.757485, rho = -0.065221
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.356148
obj = -6.623925, rho = -0.045589
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.322897
obj = -7.613228, rho = -0.099796
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.290594
obj = -8.762659, rho = -0.125238
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.262989
obj = -10.060247, rho = -0.233232
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.244870
obj = -11.448611, rho = -0.271769
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.217843
obj = -12.957489, rho = -0.222891
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.191741
obj = -14.725011, rho = -0.162591
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.173815
obj = -16.682589, rho = -0.026270
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 295
nu = 0.153943
obj = -18.788837, rho = 0.027522
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.135395
obj = -21.253047, rho = 0.001790
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.121140
obj = -24.028170, rho = 0.011729
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.109478
obj = -27.094695, rho = 0.015113
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.098459
obj = -30.201165, rho = 0.093842
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.090122
obj = -33.069347, rho = 0.304451
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.083157
obj = -35.049824, rho = 0.493575
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.069762
obj = -35.911127, rho = 0.584372
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.513706
obj = -3.367647, rho = -0.220336
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.454829
obj = -3.773906, rho = -0.210820
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.401113
obj = -4.227294, rho = -0.216282
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.353873
obj = -4.696193, rho = -0.149376
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.308642
obj = -5.220284, rho = -0.125456
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.273253
obj = -5.800280, rho = -0.062483
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 115
nu = 0.244839
obj = -6.362576, rho = -0.139233
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*..*
optimization finished, #iter = 278
nu = 0.206955
obj = -6.959219, rho = -0.159357
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.177075
obj = -7.614671, rho = -0.178035
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.149286
obj = -8.409475, rho = -0.189763
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.131295
obj = -9.319925, rho = -0.206073
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.112440
obj = -10.312242, rho = -0.219317
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.102128
obj = -11.365280, rho = -0.093235
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.088710
obj = -12.307407, rho = -0.099687
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.074438
obj = -13.303853, rho = -0.153067
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.061688
obj = -14.522237, rho = -0.158035
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.051664
obj = -16.067670, rho = -0.162855
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.043963
obj = -18.023663, rho = -0.195936
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.038227
obj = -20.420394, rho = -0.298256
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.033738
obj = -23.290008, rho = -0.423681
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.595466
obj = -4.139774, rho = -0.188766
nSV = 63, nBSV = 58
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.531242
obj = -4.741601, rho = -0.171155
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.482208
obj = -5.438919, rho = -0.116372
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.439475
obj = -6.203714, rho = -0.073137
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.397290
obj = -7.042804, rho = -0.125804
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.353672
obj = -7.964510, rho = -0.097925
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.309261
obj = -9.039634, rho = -0.056092
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.277106
obj = -10.277949, rho = -0.024254
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.248047
obj = -11.640421, rho = -0.087403
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.220205
obj = -13.190076, rho = -0.163223
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.195612
obj = -14.991465, rho = -0.134267
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.175524
obj = -17.006784, rho = -0.194545
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.158453
obj = -19.201736, rho = -0.199424
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.137567
obj = -21.701352, rho = -0.258440
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.123771
obj = -24.563454, rho = -0.329970
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.109265
obj = -27.760475, rho = -0.344427
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.096331
obj = -31.465120, rho = -0.378593
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 283
nu = 0.083591
obj = -35.845802, rho = -0.380035
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.*
optimization finished, #iter = 496
nu = 0.074433
obj = -41.239890, rho = -0.367242
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*.....*
optimization finished, #iter = 847
nu = 0.065436
obj = -47.819337, rho = -0.342692
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.570256
obj = -3.871646, rho = -0.205596
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.511475
obj = -4.390508, rho = -0.164070
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.457141
obj = -4.972140, rho = -0.171807
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.412476
obj = -5.594681, rho = -0.145929
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.365639
obj = -6.249887, rho = -0.118448
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.321382
obj = -6.977013, rho = -0.107921
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.280408
obj = -7.815320, rho = -0.153136
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.245588
obj = -8.748292, rho = -0.146197
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.221212
obj = -9.735551, rho = -0.256815
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.193350
obj = -10.714074, rho = -0.345094
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.172400
obj = -11.773077, rho = -0.511636
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.148744
obj = -12.687538, rho = -0.628362
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.128429
obj = -13.683355, rho = -0.583334
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.**.*
optimization finished, #iter = 181
nu = 0.109217
obj = -14.601121, rho = -0.443020
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 309
nu = 0.090163
obj = -15.519234, rho = -0.399216
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.075502
obj = -16.619400, rho = -0.374297
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.064213
obj = -17.649722, rho = -0.363907
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.053262
obj = -18.739439, rho = -0.422798
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.045561
obj = -19.816752, rho = -0.566351
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.040715
obj = -20.364262, rho = -0.540058
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.579055
obj = -4.040271, rho = -0.190533
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.521125
obj = -4.644676, rho = -0.217387
nSV = 54, nBSV = 52
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.468876
obj = -5.324616, rho = -0.130086
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.425920
obj = -6.112236, rho = -0.091832
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.392232
obj = -6.975671, rho = -0.065544
nSV = 40, nBSV = 38
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.352165
obj = -7.882198, rho = -0.129523
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.312599
obj = -8.881185, rho = -0.124020
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.281605
obj = -9.995281, rho = -0.243765
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.245192
obj = -11.210123, rho = -0.269863
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.214850
obj = -12.627529, rho = -0.303628
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.188677
obj = -14.254101, rho = -0.326970
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.169706
obj = -16.021650, rho = -0.416062
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.150628
obj = -17.994292, rho = -0.294981
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.130330
obj = -20.197264, rho = -0.301549
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.113550
obj = -22.844957, rho = -0.235833
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.102484
obj = -25.890985, rho = -0.199223
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.094185
obj = -28.941920, rho = -0.216798
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.082774
obj = -32.050130, rho = -0.401115
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.073988
obj = -35.123301, rho = -0.481884
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.066218
obj = -37.936228, rho = -0.558699
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.640000
obj = -4.419598, rho = -0.398233
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.584852
obj = -5.027928, rho = -0.309414
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.516059
obj = -5.714349, rho = -0.314487
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.467221
obj = -6.472029, rho = -0.236096
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.416000
obj = -7.293300, rho = -0.251501
nSV = 46, nBSV = 37
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 169
nu = 0.362119
obj = -8.270901, rho = -0.227260
nSV = 41, nBSV = 32
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.328817
obj = -9.393734, rho = -0.186704
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.295811
obj = -10.502192, rho = -0.119844
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.260000
obj = -11.800914, rho = -0.108099
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.228341
obj = -13.151759, rho = -0.066604
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.200621
obj = -14.691718, rho = -0.082567
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.175672
obj = -16.427072, rho = -0.140019
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 241
nu = 0.153700
obj = -18.353417, rho = -0.228788
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.131523
obj = -20.678307, rho = -0.205470
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.115698
obj = -23.520685, rho = -0.177902
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.105677
obj = -26.624106, rho = -0.104963
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.095589
obj = -29.777695, rho = -0.215334
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.082876
obj = -33.221520, rho = -0.236021
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.071544
obj = -37.328325, rho = -0.180646
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.065291
obj = -42.021609, rho = -0.134583
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.569620
obj = -3.945566, rho = -0.241220
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.504121
obj = -4.527723, rho = -0.226718
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.454468
obj = -5.206804, rho = -0.185275
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.413640
obj = -5.988898, rho = -0.167277
nSV = 46, nBSV = 37
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.378309
obj = -6.873615, rho = -0.074948
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.342652
obj = -7.848133, rho = -0.047457
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.312313
obj = -8.883080, rho = -0.007395
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.277644
obj = -9.996477, rho = 0.022119
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.246744
obj = -11.220956, rho = 0.092590
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.211392
obj = -12.650632, rho = 0.104022
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.189695
obj = -14.385338, rho = 0.063487
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.167275
obj = -16.278893, rho = 0.065872
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.151508
obj = -18.431394, rho = 0.024802
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.132857
obj = -20.741107, rho = 0.039938
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.119477
obj = -23.362398, rho = 0.146521
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.106833
obj = -26.248065, rho = 0.158620
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.095850
obj = -29.180541, rho = 0.073370
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.085377
obj = -31.990452, rho = 0.005234
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.075244
obj = -34.648338, rho = -0.023465
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.065048
obj = -36.893562, rho = 0.015959
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.620826
obj = -4.448658, rho = -0.328780
nSV = 65, nBSV = 60
Total nSV = 65
Accuracy = 93% (93/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.553834
obj = -5.164156, rho = -0.349318
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 92% (92/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.509251
obj = -6.012875, rho = -0.329667
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 93% (93/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.466959
obj = -6.986042, rho = -0.306421
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.420431
obj = -8.127224, rho = -0.315835
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 94% (94/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.384309
obj = -9.476096, rho = -0.266281
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 94% (94/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.343689
obj = -11.108765, rho = -0.245696
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 94% (94/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.321379
obj = -13.051660, rho = -0.139385
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.295852
obj = -15.296343, rho = -0.102811
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.267938
obj = -17.976545, rho = -0.104221
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.242981
obj = -21.289453, rho = -0.149794
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.227792
obj = -25.261348, rho = -0.152259
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.214974
obj = -29.891634, rho = -0.122996
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.199818
obj = -35.134675, rho = -0.058941
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.182788
obj = -41.411468, rho = 0.044225
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.169453
obj = -48.904976, rho = 0.132306
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.154415
obj = -57.955654, rho = 0.108410
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.142765
obj = -69.050914, rho = 0.103693
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.136059
obj = -82.195169, rho = 0.144214
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.129811
obj = -96.694463, rho = 0.219251
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.610520
obj = -4.180393, rho = -0.234420
nSV = 63, nBSV = 57
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.551551
obj = -4.754736, rho = -0.180716
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.490079
obj = -5.399508, rho = -0.220503
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.435685
obj = -6.122962, rho = -0.251716
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.386535
obj = -6.973177, rho = -0.230206
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.339875
obj = -7.964077, rho = -0.219153
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.303427
obj = -9.157799, rho = -0.174248
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.270573
obj = -10.556295, rho = -0.153979
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.251969
obj = -12.139463, rho = -0.089386
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.225882
obj = -13.937674, rho = -0.151718
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.205381
obj = -15.955341, rho = -0.099403
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.187333
obj = -18.103563, rho = -0.002746
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.172107
obj = -20.295672, rho = -0.177493
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*..........*
optimization finished, #iter = 1045
nu = 0.148591
obj = -22.739326, rho = -0.195155
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.132897
obj = -25.411280, rho = -0.285508
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.116723
obj = -28.407609, rho = -0.381318
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.105623
obj = -31.335286, rho = -0.537082
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.092881
obj = -34.041398, rho = -0.553066
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.081729
obj = -36.363695, rho = -0.581984
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*..*
optimization finished, #iter = 497
nu = 0.070274
obj = -38.230077, rho = -0.718323
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.627730
obj = -4.285745, rho = -0.184683
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.561850
obj = -4.872385, rho = -0.213064
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.497220
obj = -5.552185, rho = -0.195401
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.449285
obj = -6.330133, rho = -0.190399
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.402301
obj = -7.174847, rho = -0.199190
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.358569
obj = -8.125282, rho = -0.198849
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.320856
obj = -9.205036, rho = -0.180032
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.295526
obj = -10.361671, rho = -0.154966
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.262920
obj = -11.444274, rho = -0.144064
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.230446
obj = -12.583781, rho = -0.160734
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.197380
obj = -13.782636, rho = -0.192511
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.168876
obj = -15.141008, rho = -0.218992
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 183
nu = 0.149786
obj = -16.546627, rho = -0.210213
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.128647
obj = -17.979016, rho = -0.200221
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*..*
optimization finished, #iter = 479
nu = 0.109258
obj = -19.447840, rho = -0.120874
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.091901
obj = -21.104257, rho = -0.108783
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.079354
obj = -22.932765, rho = -0.211711
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.070066
obj = -24.544714, rho = -0.307268
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.060136
obj = -25.862608, rho = -0.499305
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.048881
obj = -26.998822, rho = -0.531434
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.629807
obj = -4.442715, rho = -0.098860
nSV = 66, nBSV = 62
Total nSV = 66
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.576408
obj = -5.086476, rho = -0.037182
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.520682
obj = -5.820456, rho = 0.004422
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.466749
obj = -6.645787, rho = -0.025632
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.414115
obj = -7.603590, rho = -0.034430
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.376930
obj = -8.696217, rho = 0.015002
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.336640
obj = -9.929867, rho = 0.006857
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.303226
obj = -11.331468, rho = 0.002791
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.272287
obj = -12.935228, rho = -0.037734
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.253181
obj = -14.648026, rho = 0.061980
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.221046
obj = -16.368898, rho = 0.095425
nSV = 27, nBSV = 16
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*...*
optimization finished, #iter = 337
nu = 0.193471
obj = -18.456228, rho = 0.135136
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.174481
obj = -20.619082, rho = 0.214312
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.151993
obj = -22.983003, rho = 0.253945
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*
optimization finished, #iter = 403
nu = 0.132523
obj = -25.699478, rho = 0.272380
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.121720
obj = -28.617943, rho = 0.337097
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 198
nu = 0.105357
obj = -31.356698, rho = 0.383802
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.090188
obj = -34.335394, rho = 0.359252
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.079409
obj = -37.516690, rho = 0.331176
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.071358
obj = -40.033222, rho = 0.372991
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.583213
obj = -3.920029, rho = -0.356282
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.514543
obj = -4.443488, rho = -0.386206
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.466008
obj = -5.020468, rho = -0.376131
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.413902
obj = -5.638980, rho = -0.382996
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.367194
obj = -6.305423, rho = -0.335702
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.322690
obj = -7.064051, rho = -0.329390
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 201
nu = 0.282331
obj = -7.915547, rho = -0.276132
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.245611
obj = -8.911359, rho = -0.285107
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.219482
obj = -10.047670, rho = -0.342277
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.189705
obj = -11.355496, rho = -0.357348
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.173201
obj = -12.796751, rho = -0.376863
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.153614
obj = -14.309098, rho = -0.499476
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.134770
obj = -15.963149, rho = -0.586368
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.118903
obj = -17.758190, rho = -0.648726
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.101673
obj = -19.830988, rho = -0.629550
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.091528
obj = -22.195900, rho = -0.521943
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.085129
obj = -24.388948, rho = -0.431393
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.073894
obj = -25.958282, rho = -0.424332
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.062236
obj = -27.505853, rho = -0.439919
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.055076
obj = -28.749712, rho = -0.430714
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.555283
obj = -3.705974, rho = -0.051391
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.505331
obj = -4.168198, rho = -0.078875
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.443917
obj = -4.650407, rho = -0.093652
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.389867
obj = -5.187488, rho = -0.069658
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.339398
obj = -5.776234, rho = -0.047108
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.294398
obj = -6.456486, rho = -0.025099
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.257703
obj = -7.249873, rho = -0.045610
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.221039
obj = -8.183883, rho = -0.023898
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.197241
obj = -9.307678, rho = 0.038948
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.181347
obj = -10.503525, rho = 0.053317
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.159011
obj = -11.788118, rho = 0.085562
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.141322
obj = -13.238921, rho = 0.098031
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 283
nu = 0.124741
obj = -14.747466, rho = 0.115213
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 213
nu = 0.108899
obj = -16.429169, rho = 0.167131
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.093871
obj = -18.422659, rho = 0.108084
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.084774
obj = -20.642610, rho = 0.041258
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.074923
obj = -22.898439, rho = -0.017797
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.067597
obj = -25.038349, rho = 0.008476
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.061524
obj = -26.756869, rho = -0.197953
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.050727
obj = -27.925358, rho = -0.171333
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.574483
obj = -3.949508, rho = 0.105516
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 96% (96/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.520000
obj = -4.489328, rho = 0.126385
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.459688
obj = -5.098404, rho = 0.107953
nSV = 50, nBSV = 41
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.410783
obj = -5.818194, rho = 0.109835
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.363613
obj = -6.645543, rho = 0.112915
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.327970
obj = -7.601792, rho = 0.093273
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.296551
obj = -8.668375, rho = 0.083817
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.272325
obj = -9.814593, rho = 0.119558
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.240957
obj = -10.994052, rho = 0.180093
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.210167
obj = -12.400231, rho = 0.176245
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.187191
obj = -13.966386, rho = 0.171518
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.161515
obj = -15.777347, rho = 0.169082
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.143910
obj = -17.949131, rho = 0.147646
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.126840
obj = -20.471441, rho = 0.105761
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.113780
obj = -23.409813, rho = 0.031948
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.104763
obj = -26.600044, rho = 0.030629
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.097780
obj = -29.808850, rho = -0.029052
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.087235
obj = -32.588762, rho = -0.062969
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.075672
obj = -35.362766, rho = -0.044112
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*..*
optimization finished, #iter = 417
nu = 0.065611
obj = -37.963562, rho = -0.111165
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.653126
obj = -4.478207, rho = -0.044387
nSV = 67, nBSV = 62
Total nSV = 67
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.579223
obj = -5.118631, rho = -0.062852
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.511994
obj = -5.874636, rho = -0.063491
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.460000
obj = -6.791275, rho = -0.100296
nSV = 47, nBSV = 45
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.426064
obj = -7.817315, rho = -0.161483
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.378996
obj = -8.966992, rho = -0.229930
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.342473
obj = -10.327443, rho = -0.290123
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.313409
obj = -11.881547, rho = -0.286961
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 224
nu = 0.276207
obj = -13.640025, rho = -0.293994
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.249830
obj = -15.789624, rho = -0.260115
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.223605
obj = -18.252713, rho = -0.238012
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.200990
obj = -21.245918, rho = -0.187966
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.183949
obj = -24.861582, rho = -0.290283
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.170248
obj = -28.949323, rho = -0.209700
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.153414
obj = -33.784222, rho = -0.228848
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.143559
obj = -39.513584, rho = -0.256356
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*...*
optimization finished, #iter = 491
nu = 0.131619
obj = -45.706159, rho = -0.150646
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.120091
obj = -53.008438, rho = -0.051676
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 442
nu = 0.107319
obj = -61.485944, rho = -0.015353
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 294
nu = 0.096806
obj = -71.902085, rho = 0.016516
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.571147
obj = -3.977038, rho = -0.153897
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.521670
obj = -4.550603, rho = -0.168654
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.467470
obj = -5.192513, rho = -0.118210
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.425887
obj = -5.885694, rho = -0.120195
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.381037
obj = -6.634860, rho = -0.128457
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.337314
obj = -7.458934, rho = -0.147107
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.300073
obj = -8.383086, rho = -0.207820
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.261956
obj = -9.428546, rho = -0.162551
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.233253
obj = -10.592689, rho = -0.104916
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.207259
obj = -11.831760, rho = -0.099570
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.183859
obj = -13.147541, rho = -0.032287
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.159559
obj = -14.603807, rho = 0.003857
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 314
nu = 0.140168
obj = -16.209270, rho = -0.019575
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 337
nu = 0.123973
obj = -17.850872, rho = -0.055877
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.108672
obj = -19.454716, rho = -0.049034
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*.*
optimization finished, #iter = 408
nu = 0.094071
obj = -20.904210, rho = -0.075209
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 391
nu = 0.077321
obj = -22.585244, rho = -0.075316
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 250
nu = 0.064532
obj = -24.695766, rho = -0.065205
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 380
nu = 0.054818
obj = -27.257266, rho = -0.075936
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 288
nu = 0.046866
obj = -30.395201, rho = -0.069450
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.552184
obj = -3.709118, rho = -0.107354
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.493607
obj = -4.203284, rho = -0.139680
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.439999
obj = -4.742830, rho = -0.118014
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.392038
obj = -5.337479, rho = -0.042986
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.346622
obj = -6.002891, rho = -0.073005
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.314002
obj = -6.693509, rho = -0.099581
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.272363
obj = -7.414655, rho = -0.089339
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.240711
obj = -8.183851, rho = -0.161709
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.210884
obj = -9.016957, rho = -0.200484
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.182373
obj = -9.896105, rho = -0.196621
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*...*
optimization finished, #iter = 324
nu = 0.155468
obj = -10.815157, rho = -0.133218
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.134525
obj = -11.829624, rho = -0.155140
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.114085
obj = -12.923522, rho = -0.189762
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.098832
obj = -14.203222, rho = -0.218128
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 277
nu = 0.083814
obj = -15.564907, rho = -0.233359
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*.*
optimization finished, #iter = 314
nu = 0.070360
obj = -17.256663, rho = -0.223939
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 261
nu = 0.061638
obj = -19.311793, rho = -0.222625
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 287
nu = 0.055459
obj = -21.490536, rho = -0.265518
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 317
nu = 0.048796
obj = -23.521945, rho = -0.291867
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 296
nu = 0.041113
obj = -25.879527, rho = -0.288064
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.536378
obj = -3.751987, rho = -0.099933
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.491891
obj = -4.292343, rho = -0.066502
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.440062
obj = -4.882515, rho = -0.138473
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.398634
obj = -5.557411, rho = -0.204819
nSV = 41, nBSV = 38
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.355387
obj = -6.278507, rho = -0.140664
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.312387
obj = -7.108267, rho = -0.197490
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.276145
obj = -8.091362, rho = -0.194887
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 188
nu = 0.242265
obj = -9.239208, rho = -0.191286
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.217315
obj = -10.617832, rho = -0.258079
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.194419
obj = -12.234537, rho = -0.344119
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.175670
obj = -14.168377, rho = -0.406544
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.158378
obj = -16.413137, rho = -0.414401
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.145466
obj = -18.989962, rho = -0.336612
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.130899
obj = -21.919034, rho = -0.267651
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.120000
obj = -25.365550, rho = -0.242514
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.109760
obj = -29.161286, rho = -0.121063
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.097498
obj = -33.569818, rho = -0.105993
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.086750
obj = -38.870433, rho = -0.141112
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.077188
obj = -45.474900, rho = -0.202647
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.072049
obj = -53.447769, rho = -0.241292
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.587444
obj = -4.021645, rho = -0.255614
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.529749
obj = -4.566596, rho = -0.229961
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.467155
obj = -5.193950, rho = -0.263781
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.421334
obj = -5.898016, rho = -0.325386
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.370826
obj = -6.702874, rho = -0.317726
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.340000
obj = -7.648124, rho = -0.341635
nSV = 34, nBSV = 33
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.305670
obj = -8.599291, rho = -0.338702
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.275664
obj = -9.599548, rho = -0.340629
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.242582
obj = -10.672873, rho = -0.359721
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.212794
obj = -11.771990, rho = -0.371512
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 285
nu = 0.184701
obj = -12.899822, rho = -0.382561
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 228
nu = 0.158185
obj = -14.214006, rho = -0.350223
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.137285
obj = -15.628004, rho = -0.347070
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.117464
obj = -17.228139, rho = -0.349682
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.102321
obj = -18.919767, rho = -0.362963
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 246
nu = 0.086802
obj = -20.906226, rho = -0.284743
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.078343
obj = -23.014491, rho = -0.267357
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*..*
optimization finished, #iter = 230
nu = 0.066586
obj = -25.010507, rho = -0.283873
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.055831
obj = -27.448893, rho = -0.278409
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.048004
obj = -30.391528, rho = -0.275107
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.532464
obj = -3.609649, rho = -0.269196
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.479428
obj = -4.085510, rho = -0.216253
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.422189
obj = -4.620908, rho = -0.200942
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.381582
obj = -5.224761, rho = -0.210420
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.335384
obj = -5.888191, rho = -0.188349
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.308425
obj = -6.571394, rho = -0.134761
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.268824
obj = -7.281660, rho = -0.122542
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.237347
obj = -8.041007, rho = -0.099329
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.208649
obj = -8.777061, rho = -0.052925
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.175189
obj = -9.574287, rho = -0.045514
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 188
nu = 0.147901
obj = -10.530117, rho = -0.052386
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.126894
obj = -11.684875, rho = -0.029903
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.113050
obj = -12.905095, rho = 0.060343
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.100553
obj = -14.105418, rho = 0.149172
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.086594
obj = -15.240408, rho = 0.165328
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.073698
obj = -16.456515, rho = 0.174462
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.062223
obj = -17.618871, rho = 0.202122
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.052850
obj = -18.934591, rho = 0.259698
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.047299
obj = -19.943483, rho = 0.379616
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.....*
optimization finished, #iter = 627
nu = 0.039485
obj = -20.414000, rho = 0.449434
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.600000
obj = -3.924815, rho = -0.067257
nSV = 60, nBSV = 60
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.525670
obj = -4.392175, rho = -0.078561
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.463274
obj = -4.932314, rho = -0.097063
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.411882
obj = -5.524045, rho = -0.087951
nSV = 42, nBSV = 39
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.361243
obj = -6.165325, rho = -0.130697
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.322783
obj = -6.854505, rho = -0.080859
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.280074
obj = -7.595433, rho = -0.097798
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.247292
obj = -8.371906, rho = -0.024332
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.214703
obj = -9.172461, rho = 0.007181
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.182425
obj = -10.051844, rho = -0.020136
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.154446
obj = -11.117434, rho = -0.020204
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.136037
obj = -12.345926, rho = -0.061840
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.120638
obj = -13.529328, rho = -0.134845
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.103891
obj = -14.801782, rho = -0.159777
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.091961
obj = -16.058859, rho = -0.122529
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.082423
obj = -16.948136, rho = -0.108847
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*...*....*
optimization finished, #iter = 769
nu = 0.068302
obj = -17.492940, rho = -0.080159
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.055709
obj = -17.868330, rho = -0.031300
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*......*
optimization finished, #iter = 755
nu = 0.044461
obj = -18.230509, rho = -0.035980
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 194
nu = 0.035572
obj = -18.632720, rho = -0.094373
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.563451
obj = -3.776501, rho = -0.247464
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.499937
obj = -4.268310, rho = -0.234808
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.447401
obj = -4.814817, rho = -0.262395
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.394005
obj = -5.423134, rho = -0.250274
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.345353
obj = -6.130937, rho = -0.279984
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.303472
obj = -6.957677, rho = -0.303824
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.273476
obj = -7.920643, rho = -0.293272
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.248999
obj = -8.941773, rho = -0.369141
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.215980
obj = -10.065323, rho = -0.384883
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.190653
obj = -11.405057, rho = -0.445601
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.169055
obj = -12.932221, rho = -0.484222
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.152560
obj = -14.645406, rho = -0.526638
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 266
nu = 0.135396
obj = -16.543775, rho = -0.547693
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.123409
obj = -18.597517, rho = -0.585015
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.109999
obj = -20.653138, rho = -0.587842
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 274
nu = 0.095693
obj = -22.767740, rho = -0.636084
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*..*
optimization finished, #iter = 431
nu = 0.080501
obj = -25.287710, rho = -0.627463
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.070337
obj = -28.431372, rho = -0.644644
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.065821
obj = -31.504541, rho = -0.717890
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
....*
optimization finished, #iter = 472
nu = 0.060534
obj = -33.577416, rho = -0.839447
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.591324
obj = -4.066811, rho = -0.097800
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.520279
obj = -4.658411, rho = -0.121020
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.476419
obj = -5.344424, rho = -0.175153
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.425088
obj = -6.119780, rho = -0.167079
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.379777
obj = -7.023562, rho = -0.130473
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.344730
obj = -8.063868, rho = -0.084482
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.315685
obj = -9.174108, rho = -0.187135
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.281747
obj = -10.419945, rho = -0.239368
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.249677
obj = -11.844465, rho = -0.199776
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.223084
obj = -13.461898, rho = -0.278131
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.208208
obj = -15.216027, rho = -0.393848
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.180850
obj = -16.986850, rho = -0.431271
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.159350
obj = -19.050626, rho = -0.435830
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.139662
obj = -21.371421, rho = -0.489052
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.121908
obj = -24.020499, rho = -0.510403
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.106742
obj = -27.167222, rho = -0.595070
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.095902
obj = -30.658858, rho = -0.631543
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.089122
obj = -34.264714, rho = -0.703731
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.081294
obj = -37.231839, rho = -0.794274
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.070588
obj = -39.475439, rho = -0.907027
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.595154
obj = -4.028497, rho = -0.234273
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.535125
obj = -4.578732, rho = -0.218230
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.479761
obj = -5.178953, rho = -0.193584
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.424751
obj = -5.821005, rho = -0.177547
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.377080
obj = -6.565722, rho = -0.134804
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.337156
obj = -7.358297, rho = -0.125501
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.291113
obj = -8.245378, rho = -0.114771
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.253869
obj = -9.308814, rho = -0.107106
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.230359
obj = -10.512122, rho = -0.168616
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.205055
obj = -11.752474, rho = -0.182484
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.179111
obj = -13.141402, rho = -0.180092
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.156667
obj = -14.643792, rho = -0.211142
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.136788
obj = -16.399596, rho = -0.215753
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.122031
obj = -18.268218, rho = -0.210217
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.108355
obj = -20.250101, rho = -0.259174
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.093502
obj = -22.366458, rho = -0.160072
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.079505
obj = -24.844690, rho = -0.136767
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.068809
obj = -27.718775, rho = -0.101395
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.058783
obj = -31.288065, rho = -0.107390
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.050713
obj = -35.795286, rho = -0.107970
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.531118
obj = -3.583341, rho = -0.139074
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.471628
obj = -4.066439, rho = -0.134883
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.429600
obj = -4.597169, rho = -0.135367
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.387087
obj = -5.141028, rho = -0.184613
nSV = 42, nBSV = 33
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.335402
obj = -5.751371, rho = -0.218323
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.290903
obj = -6.452826, rho = -0.206949
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.254282
obj = -7.258367, rho = -0.152960
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.228662
obj = -8.184712, rho = -0.065495
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.201243
obj = -9.175655, rho = -0.116469
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.176076
obj = -10.308448, rho = -0.111284
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.155347
obj = -11.614895, rho = -0.105179
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.135578
obj = -13.122173, rho = -0.170921
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.121105
obj = -14.832126, rho = -0.217216
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.106136
obj = -16.821654, rho = -0.228965
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.097911
obj = -18.941454, rho = -0.254317
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.086955
obj = -21.094350, rho = -0.231753
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.079246
obj = -23.291697, rho = -0.345499
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.071797
obj = -25.104258, rho = -0.441007
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.061364
obj = -26.226847, rho = -0.517802
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.049772
obj = -27.314399, rho = -0.546340
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.592074
obj = -4.269190, rho = -0.189564
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.552995
obj = -4.929714, rho = -0.123134
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.494522
obj = -5.669217, rho = -0.104218
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.441867
obj = -6.540909, rho = -0.172172
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.398244
obj = -7.577246, rho = -0.233069
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.362114
obj = -8.793475, rho = -0.280338
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.327881
obj = -10.220141, rho = -0.345189
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.300006
obj = -11.896322, rho = -0.296651
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.283593
obj = -13.755705, rho = -0.306932
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.251232
obj = -15.816667, rho = -0.250100
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.231653
obj = -18.195380, rho = -0.279687
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.208019
obj = -20.876582, rho = -0.373735
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.186185
obj = -23.931367, rho = -0.336353
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.162999
obj = -27.661017, rho = -0.358118
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.150355
obj = -32.054077, rho = -0.549002
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.136919
obj = -37.182772, rho = -0.631272
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.126316
obj = -42.845562, rho = -0.741825
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.118438
obj = -49.023931, rho = -0.772240
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.108612
obj = -55.182197, rho = -0.791242
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.095689
obj = -61.293300, rho = -0.809010
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.556896
obj = -3.936157, rho = -0.360653
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.503712
obj = -4.528289, rho = -0.388537
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.458603
obj = -5.203294, rho = -0.351564
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.425615
obj = -5.944105, rho = -0.298352
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.378227
obj = -6.743566, rho = -0.315775
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.340000
obj = -7.643052, rho = -0.315397
nSV = 36, nBSV = 32
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.300695
obj = -8.609163, rho = -0.334484
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.267123
obj = -9.736551, rho = -0.342310
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.240231
obj = -10.970674, rho = -0.316815
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.214835
obj = -12.312509, rho = -0.375613
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.186431
obj = -13.737993, rho = -0.402295
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.163384
obj = -15.375484, rho = -0.373083
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.142519
obj = -17.309403, rho = -0.364114
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.128023
obj = -19.456669, rho = -0.412494
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.113644
obj = -21.650125, rho = -0.501449
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.099581
obj = -24.107562, rho = -0.605509
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.086955
obj = -26.695769, rho = -0.631012
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 240
nu = 0.075000
obj = -29.695644, rho = -0.650521
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 385
nu = 0.068100
obj = -32.834452, rho = -0.726682
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*.*
optimization finished, #iter = 413
nu = 0.060718
obj = -35.707033, rho = -0.862235
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.570949
obj = -3.946988, rho = 0.070040
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.520083
obj = -4.512349, rho = 0.009919
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.464714
obj = -5.125343, rho = 0.028508
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.415584
obj = -5.830625, rho = -0.001862
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.370831
obj = -6.627425, rho = -0.003748
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.337732
obj = -7.478331, rho = 0.037504
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.300319
obj = -8.388201, rho = 0.086626
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.260803
obj = -9.425823, rho = 0.036977
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.227224
obj = -10.643677, rho = -0.002822
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.209481
obj = -11.966022, rho = -0.111281
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.180575
obj = -13.357373, rho = -0.097989
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.161120
obj = -15.000977, rho = -0.032447
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.144399
obj = -16.569783, rho = -0.048388
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.125460
obj = -18.296896, rho = -0.001157
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.111059
obj = -20.062931, rho = 0.058235
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.093987
obj = -21.959249, rho = 0.040405
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.084175
obj = -23.865660, rho = 0.044742
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.073463
obj = -25.482217, rho = 0.126270
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.060422
obj = -26.917455, rho = 0.151860
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.050005
obj = -28.597485, rho = 0.172626
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.572633
obj = -3.924970, rho = -0.087298
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.507892
obj = -4.477835, rho = -0.088473
nSV = 56, nBSV = 48
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.457976
obj = -5.124153, rho = -0.195700
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.418526
obj = -5.818338, rho = -0.244949
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.372353
obj = -6.589257, rho = -0.201015
nSV = 38, nBSV = 35
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.332242
obj = -7.437114, rho = -0.248913
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.289023
obj = -8.421565, rho = -0.266182
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.260403
obj = -9.563864, rho = -0.274113
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.229489
obj = -10.874870, rho = -0.272985
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.204271
obj = -12.401988, rho = -0.252254
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.180133
obj = -14.165585, rho = -0.202007
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.162756
obj = -16.260447, rho = -0.167407
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.148270
obj = -18.571462, rho = -0.171210
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.133722
obj = -21.118628, rho = -0.188202
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.119516
obj = -23.932730, rho = -0.241602
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.106637
obj = -27.195414, rho = -0.229790
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*.*
optimization finished, #iter = 331
nu = 0.095569
obj = -30.594763, rho = -0.222324
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.085975
obj = -34.511907, rho = -0.152575
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*...........*
optimization finished, #iter = 1145
nu = 0.075927
obj = -38.425993, rho = -0.189728
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.064367
obj = -43.155521, rho = -0.195217
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.520000
obj = -3.400089, rho = -0.255705
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.462015
obj = -3.793272, rho = -0.292676
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.405203
obj = -4.231867, rho = -0.335801
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.355499
obj = -4.706911, rho = -0.327229
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.312728
obj = -5.220542, rho = -0.329091
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.272584
obj = -5.786391, rho = -0.312158
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.240874
obj = -6.393299, rho = -0.273804
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.215076
obj = -6.989454, rho = -0.300359
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.182595
obj = -7.521758, rho = -0.329999
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.154612
obj = -8.138125, rho = -0.358988
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.134727
obj = -8.722240, rho = -0.402851
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.111443
obj = -9.286381, rho = -0.407584
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.093837
obj = -9.939858, rho = -0.459228
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.080853
obj = -10.524693, rho = -0.518167
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 117
nu = 0.068729
obj = -10.991640, rho = -0.524019
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 362
nu = 0.058260
obj = -11.186145, rho = -0.544066
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.046355
obj = -11.200879, rho = -0.570775
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.036377
obj = -11.200879, rho = -0.570775
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.028547
obj = -11.200879, rho = -0.570775
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.022403
obj = -11.200879, rho = -0.570775
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.540000
obj = -3.682596, rho = -0.178827
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.484986
obj = -4.189972, rho = -0.209626
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.430791
obj = -4.766406, rho = -0.205906
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.379112
obj = -5.441655, rho = -0.217219
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.341774
obj = -6.231453, rho = -0.313973
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.303670
obj = -7.144985, rho = -0.299460
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.274590
obj = -8.196449, rho = -0.294153
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.248439
obj = -9.370589, rho = -0.232881
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.230729
obj = -10.637167, rho = -0.080222
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.200293
obj = -12.038868, rho = -0.075801
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.182454
obj = -13.599596, rho = -0.080761
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.160433
obj = -15.315428, rho = -0.045668
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.141570
obj = -17.261587, rho = -0.035693
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.127864
obj = -19.447039, rho = -0.142166
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.115103
obj = -21.524314, rho = -0.259264
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.099255
obj = -23.879616, rho = -0.165962
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.090255
obj = -26.201954, rho = 0.138148
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.079064
obj = -28.308297, rho = 0.073196
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.071150
obj = -29.842755, rho = -0.021392
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.060253
obj = -30.135510, rho = -0.061027
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.549168
obj = -3.620512, rho = -0.019991
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.492022
obj = -4.056090, rho = -0.038883
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.433575
obj = -4.526186, rho = -0.048577
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.384938
obj = -5.033471, rho = -0.051275
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.338324
obj = -5.575199, rho = -0.044419
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.295606
obj = -6.152680, rho = -0.079480
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.258101
obj = -6.728400, rho = -0.084804
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.224403
obj = -7.329263, rho = -0.112248
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.189551
obj = -7.928395, rho = -0.127981
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.165349
obj = -8.566335, rho = -0.149692
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.....*
optimization finished, #iter = 631
nu = 0.140012
obj = -9.209293, rho = -0.146151
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
..*.*
optimization finished, #iter = 303
nu = 0.119574
obj = -9.817125, rho = -0.104183
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*....*
optimization finished, #iter = 522
nu = 0.098857
obj = -10.421081, rho = -0.103204
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*
optimization finished, #iter = 268
nu = 0.081950
obj = -11.150648, rho = -0.089397
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.070039
obj = -11.898431, rho = -0.082188
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.059417
obj = -12.541368, rho = -0.090281
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.048392
obj = -13.223055, rho = -0.072628
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.040259
obj = -14.004712, rho = -0.047981
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.034373
obj = -14.708517, rho = -0.026685
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.030054
obj = -15.025274, rho = 0.042724
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.527416
obj = -3.581070, rho = -0.172299
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.473410
obj = -4.071199, rho = -0.159770
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.421621
obj = -4.604427, rho = -0.120618
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.384435
obj = -5.184350, rho = -0.080177
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.331845
obj = -5.817740, rho = -0.076002
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.301041
obj = -6.534370, rho = -0.189738
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.260805
obj = -7.298404, rho = -0.172607
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.227948
obj = -8.180171, rho = -0.216364
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.199978
obj = -9.214711, rho = -0.089715
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.175194
obj = -10.405890, rho = -0.044106
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.157102
obj = -11.762760, rho = 0.011196
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.141942
obj = -13.185882, rho = 0.086546
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*....*
optimization finished, #iter = 517
nu = 0.122313
obj = -14.723149, rho = 0.135924
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.106151
obj = -16.603625, rho = 0.177398
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.093951
obj = -18.834355, rho = 0.226876
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.084766
obj = -21.227091, rho = 0.298890
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 450
nu = 0.073950
obj = -23.886240, rho = 0.365739
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*..*
optimization finished, #iter = 302
nu = 0.065541
obj = -26.928071, rho = 0.387693
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.058862
obj = -30.345204, rho = 0.429798
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.053272
obj = -34.032338, rho = 0.522004
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.582377
obj = -3.924571, rho = -0.247854
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.520047
obj = -4.440567, rho = -0.228345
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.459724
obj = -5.027774, rho = -0.248803
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.407272
obj = -5.697964, rho = -0.230591
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.359745
obj = -6.484897, rho = -0.213665
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.333189
obj = -7.344792, rho = -0.166277
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.297352
obj = -8.202085, rho = -0.133987
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.258740
obj = -9.175961, rho = -0.130059
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.225408
obj = -10.265339, rho = -0.221742
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.199141
obj = -11.515451, rho = -0.214386
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.178753
obj = -12.853259, rho = -0.258731
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.155733
obj = -14.250606, rho = -0.275611
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.139054
obj = -15.737741, rho = -0.242601
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.121890
obj = -17.233109, rho = -0.247293
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.108021
obj = -18.644995, rho = -0.262629
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*..*
optimization finished, #iter = 419
nu = 0.092395
obj = -19.747152, rho = -0.258193
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.075881
obj = -20.889154, rho = -0.233343
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 237
nu = 0.063845
obj = -22.156290, rho = -0.311768
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.053998
obj = -23.222563, rho = -0.348609
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.045558
obj = -24.139862, rho = -0.357473
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.581249
obj = -3.848926, rho = -0.234846
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.521700
obj = -4.322024, rho = -0.242806
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.459712
obj = -4.809251, rho = -0.199292
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.403181
obj = -5.360166, rho = -0.161214
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.355576
obj = -5.962115, rho = -0.199655
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.309309
obj = -6.620204, rho = -0.222967
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.270666
obj = -7.337838, rho = -0.314552
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.239318
obj = -8.073664, rho = -0.441647
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.204938
obj = -8.885428, rho = -0.506428
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.178210
obj = -9.769687, rho = -0.461760
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.151652
obj = -10.774625, rho = -0.467789
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.136302
obj = -11.809873, rho = -0.525830
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.120882
obj = -12.710245, rho = -0.487864
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.103210
obj = -13.432456, rho = -0.468223
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.083787
obj = -14.164722, rho = -0.466769
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.070803
obj = -14.955828, rho = -0.506269
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.058480
obj = -15.738417, rho = -0.526908
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.048108
obj = -16.525395, rho = -0.512084
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.039508
obj = -17.426874, rho = -0.529614
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.034152
obj = -18.268937, rho = -0.619395
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.569785
obj = -4.002451, rho = -0.193402
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.529834
obj = -4.567003, rho = -0.137153
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.472938
obj = -5.169005, rho = -0.183878
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.418331
obj = -5.861453, rho = -0.230464
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.375234
obj = -6.621872, rho = -0.198801
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.334097
obj = -7.499508, rho = -0.266908
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.297191
obj = -8.461627, rho = -0.304546
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.261579
obj = -9.547538, rho = -0.316069
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.229884
obj = -10.804686, rho = -0.303779
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.201864
obj = -12.319189, rho = -0.277106
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.180819
obj = -14.065475, rho = -0.308792
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.163071
obj = -16.086916, rho = -0.318529
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.146552
obj = -18.293907, rho = -0.268467
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.129108
obj = -20.894368, rho = -0.319314
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.116347
obj = -23.847296, rho = -0.384112
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.106461
obj = -27.109600, rho = -0.333925
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*..*
optimization finished, #iter = 211
nu = 0.094188
obj = -30.697442, rho = -0.337866
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.083339
obj = -34.908355, rho = -0.382118
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.076170
obj = -39.518168, rho = -0.409785
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.068163
obj = -44.346895, rho = -0.509884
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.606785
obj = -4.096947, rho = -0.123152
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.549959
obj = -4.626720, rho = -0.106809
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.481311
obj = -5.211105, rho = -0.102270
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.429353
obj = -5.870291, rho = -0.070762
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.386071
obj = -6.590083, rho = -0.054144
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.334983
obj = -7.359928, rho = -0.072884
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.295271
obj = -8.242531, rho = -0.123906
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.261464
obj = -9.203260, rho = -0.084075
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 282
nu = 0.234237
obj = -10.194351, rho = -0.025994
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.200569
obj = -11.253415, rho = -0.025901
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.177879
obj = -12.435416, rho = -0.169235
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.153216
obj = -13.610227, rho = -0.230446
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.131228
obj = -14.939728, rho = -0.304388
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.119119
obj = -16.251580, rho = -0.317632
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.099711
obj = -17.430690, rho = -0.298381
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.083938
obj = -18.766080, rho = -0.329187
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.073694
obj = -20.044358, rho = -0.249021
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.064684
obj = -20.786078, rho = -0.096145
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.......*
optimization finished, #iter = 1014
nu = 0.052721
obj = -21.031674, rho = -0.069727
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
......*.*
optimization finished, #iter = 711
nu = 0.042238
obj = -21.123431, rho = -0.031921
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.544109
obj = -3.523196, rho = -0.102227
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.478026
obj = -3.917952, rho = -0.125773
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.416887
obj = -4.356264, rho = -0.090960
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.370372
obj = -4.830754, rho = -0.086670
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.319196
obj = -5.351201, rho = -0.126519
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.279590
obj = -5.938908, rho = -0.144423
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.247191
obj = -6.540661, rho = -0.106973
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.210405
obj = -7.204854, rho = -0.110227
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.182816
obj = -7.960667, rho = -0.011197
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.162227
obj = -8.754266, rho = -0.011716
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.141151
obj = -9.544037, rho = 0.003113
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.121373
obj = -10.291992, rho = 0.027775
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.102381
obj = -11.064024, rho = 0.084939
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.091854
obj = -11.716402, rho = 0.028409
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.076562
obj = -12.144153, rho = -0.007817
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.063869
obj = -12.382011, rho = -0.013155
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.051533
obj = -12.452012, rho = -0.023929
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.040441
obj = -12.452012, rho = -0.023929
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.031736
obj = -12.452012, rho = -0.023929
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.024905
obj = -12.452012, rho = -0.023929
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.619438
obj = -4.271920, rho = -0.072243
nSV = 64, nBSV = 60
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.555992
obj = -4.875783, rho = -0.039433
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.501047
obj = -5.561131, rho = -0.095663
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.451573
obj = -6.314740, rho = -0.106969
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.405032
obj = -7.170691, rho = -0.093972
nSV = 42, nBSV = 39
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.361730
obj = -8.104819, rho = -0.137766
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.317258
obj = -9.168600, rho = -0.124720
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.285317
obj = -10.384699, rho = -0.078831
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.256408
obj = -11.681924, rho = -0.111972
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*....*
optimization finished, #iter = 575
nu = 0.221959
obj = -13.156850, rho = -0.125646
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.195646
obj = -14.895338, rho = -0.104365
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.174860
obj = -16.840867, rho = -0.064784
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*....*
optimization finished, #iter = 458
nu = 0.158855
obj = -18.896663, rho = -0.110128
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.138287
obj = -21.183982, rho = -0.157171
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
...*
optimization finished, #iter = 397
nu = 0.119327
obj = -23.865463, rho = -0.165773
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*.*
optimization finished, #iter = 385
nu = 0.103946
obj = -27.092210, rho = -0.140623
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
....*.....*
optimization finished, #iter = 917
nu = 0.092309
obj = -30.948618, rho = -0.176513
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.......*.*
optimization finished, #iter = 854
nu = 0.084948
obj = -35.304473, rho = -0.211358
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*......*
optimization finished, #iter = 866
nu = 0.077766
obj = -39.740808, rho = -0.221236
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.067106
obj = -44.581269, rho = -0.222275
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.583175
obj = -3.841418, rho = -0.036886
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.520712
obj = -4.314335, rho = -0.072786
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.456900
obj = -4.832848, rho = -0.073503
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.399592
obj = -5.413591, rho = -0.083581
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.351226
obj = -6.070300, rho = -0.087371
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.306990
obj = -6.834126, rho = -0.017930
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.269735
obj = -7.714621, rho = 0.006398
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.243455
obj = -8.679732, rho = 0.022404
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.218597
obj = -9.672239, rho = 0.007160
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 253
nu = 0.188677
obj = -10.766080, rho = 0.004359
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.162896
obj = -11.997355, rho = 0.001432
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.145077
obj = -13.439634, rho = -0.071827
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.128904
obj = -14.848005, rho = -0.140906
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.110772
obj = -16.458726, rho = -0.171223
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.098171
obj = -18.182914, rho = -0.240509
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.086540
obj = -19.791526, rho = -0.346662
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.074400
obj = -21.429840, rho = -0.389545
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.062461
obj = -23.301145, rho = -0.375444
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.054405
obj = -25.173940, rho = -0.327196
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 260
nu = 0.046083
obj = -27.145713, rho = -0.232106
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.576851
obj = -3.870379, rho = -0.098375
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.517415
obj = -4.370597, rho = -0.031359
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.449769
obj = -4.928498, rho = -0.010760
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.400090
obj = -5.595141, rho = 0.043731
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.355529
obj = -6.332144, rho = 0.097158
nSV = 40, nBSV = 31
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.312077
obj = -7.212962, rho = 0.125991
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.283000
obj = -8.229481, rho = 0.108501
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 117
nu = 0.251938
obj = -9.346224, rho = 0.052642
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.224780
obj = -10.656812, rho = 0.017301
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.200139
obj = -12.143081, rho = 0.019408
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.176596
obj = -13.885286, rho = -0.044674
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.159370
obj = -15.918745, rho = -0.170336
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.144074
obj = -18.205029, rho = -0.270035
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.128166
obj = -20.840109, rho = -0.225461
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.120512
obj = -23.687091, rho = -0.157286
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.109211
obj = -26.417611, rho = -0.069288
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.095811
obj = -29.249369, rho = -0.080033
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.083990
obj = -32.303522, rho = -0.095380
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.074287
obj = -35.465479, rho = -0.104876
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.064019
obj = -38.726184, rho = -0.098319
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.547192
obj = -3.576828, rho = -0.072727
nSV = 60, nBSV = 51
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.478455
obj = -4.014065, rho = -0.041225
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.421563
obj = -4.508389, rho = -0.056107
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.374597
obj = -5.069139, rho = -0.052885
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.328558
obj = -5.684528, rho = -0.023993
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.298953
obj = -6.344606, rho = 0.053717
nSV = 30, nBSV = 28
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.261895
obj = -6.988036, rho = 0.018081
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.226531
obj = -7.690079, rho = 0.101734
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.191528
obj = -8.493611, rho = 0.166285
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.165255
obj = -9.449857, rho = 0.193552
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.142620
obj = -10.592532, rho = 0.209495
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.131125
obj = -11.782783, rho = 0.348660
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.112740
obj = -12.945740, rho = 0.426090
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.......*
optimization finished, #iter = 755
nu = 0.099412
obj = -14.224483, rho = 0.516570
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.086421
obj = -15.511766, rho = 0.540698
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 375
nu = 0.073545
obj = -16.843231, rho = 0.546167
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.....*
optimization finished, #iter = 762
nu = 0.062387
obj = -18.315257, rho = 0.586297
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*...*
optimization finished, #iter = 604
nu = 0.051883
obj = -20.091357, rho = 0.586865
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.044104
obj = -22.305255, rho = 0.573966
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.038841
obj = -24.849985, rho = 0.503814
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.616621
obj = -4.213432, rho = -0.103217
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.547697
obj = -4.804159, rho = -0.076269
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.493480
obj = -5.472601, rho = -0.160214
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.440604
obj = -6.239212, rho = -0.155090
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.387532
obj = -7.128356, rho = -0.171678
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.345516
obj = -8.197369, rho = -0.165002
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.314904
obj = -9.445477, rho = -0.267663
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.285898
obj = -10.816202, rho = -0.287291
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.262276
obj = -12.374365, rho = -0.206244
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.237012
obj = -13.965571, rho = -0.146534
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.215962
obj = -15.670506, rho = -0.039524
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.196679
obj = -17.345290, rho = -0.096855
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.170331
obj = -18.989285, rho = -0.130889
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.144244
obj = -20.762126, rho = -0.155977
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.127260
obj = -22.721208, rho = -0.178192
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.110653
obj = -24.426728, rho = -0.275101
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.091493
obj = -26.229667, rho = -0.314234
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.075606
obj = -28.487743, rho = -0.318273
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.065455
obj = -31.163785, rho = -0.357321
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.056761
obj = -33.618980, rho = -0.385182
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.584388
obj = -4.013829, rho = -0.387443
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.517773
obj = -4.597499, rho = -0.410081
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.479069
obj = -5.264871, rho = -0.399038
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.424880
obj = -5.979166, rho = -0.362647
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.384716
obj = -6.740369, rho = -0.288424
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.340389
obj = -7.578892, rho = -0.245948
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.295363
obj = -8.565645, rho = -0.242216
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.272108
obj = -9.688659, rho = -0.225287
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.240917
obj = -10.824355, rho = -0.157010
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.211384
obj = -12.025263, rho = -0.101107
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 337
nu = 0.179837
obj = -13.456432, rho = -0.096963
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.160371
obj = -15.179190, rho = -0.112098
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.139949
obj = -17.052146, rho = -0.138418
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 309
nu = 0.121130
obj = -19.318936, rho = -0.154953
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.106578
obj = -22.076345, rho = -0.181430
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 349
nu = 0.099429
obj = -25.112739, rho = -0.261412
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.....*...*
optimization finished, #iter = 899
nu = 0.089015
obj = -28.240186, rho = -0.259147
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.077611
obj = -31.864776, rho = -0.264407
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*
optimization finished, #iter = 456
nu = 0.066768
obj = -36.158061, rho = -0.258207
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.059365
obj = -41.534089, rho = -0.225739
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.493339
obj = -3.286040, rho = -0.153593
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.440067
obj = -3.705164, rho = -0.189974
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.389303
obj = -4.153146, rho = -0.189230
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.343628
obj = -4.668758, rho = -0.136497
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.298900
obj = -5.255149, rho = -0.210724
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.266901
obj = -5.917952, rho = -0.205790
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.233554
obj = -6.670678, rho = -0.221702
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.209780
obj = -7.518708, rho = -0.295748
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.188071
obj = -8.404650, rho = -0.353877
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.162661
obj = -9.367830, rho = -0.420863
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.141635
obj = -10.518581, rho = -0.440004
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.124515
obj = -11.787114, rho = -0.488502
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.110565
obj = -13.235649, rho = -0.559535
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.095865
obj = -14.895244, rho = -0.558827
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.083401
obj = -16.829419, rho = -0.557365
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.072811
obj = -19.223504, rho = -0.594207
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.068356
obj = -21.855828, rho = -0.800863
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 115
nu = 0.062913
obj = -24.447524, rho = -1.056611
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.056583
obj = -26.587089, rho = -1.304808
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*..*
optimization finished, #iter = 219
nu = 0.047678
obj = -28.917317, rho = -1.328852
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.536839
obj = -3.507063, rho = -0.288516
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.474521
obj = -3.927099, rho = -0.281416
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.419275
obj = -4.381654, rho = -0.280790
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.367193
obj = -4.874302, rho = -0.255925
nSV = 41, nBSV = 32
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.318797
obj = -5.436575, rho = -0.231579
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.282417
obj = -6.057449, rho = -0.240120
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.250085
obj = -6.718989, rho = -0.169318
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.218565
obj = -7.386021, rho = -0.190000
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.188232
obj = -8.109897, rho = -0.195553
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.165971
obj = -8.879115, rho = -0.290020
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.140872
obj = -9.662928, rho = -0.276006
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.121509
obj = -10.551791, rho = -0.244842
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.104793
obj = -11.399707, rho = -0.254144
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.087588
obj = -12.362294, rho = -0.249814
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.075592
obj = -13.367890, rho = -0.259868
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.066687
obj = -14.361939, rho = -0.328799
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.058996
obj = -14.885095, rho = -0.412669
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.048476
obj = -15.115036, rho = -0.332189
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.038543
obj = -15.122849, rho = -0.302211
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.030247
obj = -15.122849, rho = -0.302211
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.553437
obj = -3.838410, rho = 0.046429
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.500000
obj = -4.383541, rho = -0.013540
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.451989
obj = -4.989859, rho = -0.053569
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.416995
obj = -5.630193, rho = -0.161347
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.367086
obj = -6.287696, rho = -0.159147
nSV = 42, nBSV = 33
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.319312
obj = -7.051647, rho = -0.174012
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.282056
obj = -7.924992, rho = -0.165086
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.246645
obj = -8.929561, rho = -0.158266
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.214819
obj = -10.107465, rho = -0.132923
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.191227
obj = -11.487760, rho = -0.085401
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.167690
obj = -13.066964, rho = -0.072201
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*..*
optimization finished, #iter = 235
nu = 0.146696
obj = -15.015263, rho = -0.074820
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.130545
obj = -17.423244, rho = -0.086383
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.119230
obj = -20.299672, rho = -0.149140
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.111056
obj = -23.574155, rho = -0.245348
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.104309
obj = -27.038558, rho = -0.369691
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.096280
obj = -30.543417, rho = -0.470478
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.086714
obj = -34.040181, rho = -0.579546
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.073392
obj = -38.049604, rho = -0.553055
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.063184
obj = -43.054373, rho = -0.517593
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.574159
obj = -3.718390, rho = -0.218943
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.500490
obj = -4.156393, rho = -0.194109
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.431274
obj = -4.669610, rho = -0.207675
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.387108
obj = -5.253511, rho = -0.184246
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.341475
obj = -5.886273, rho = -0.132267
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.299605
obj = -6.593856, rho = -0.102509
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.266055
obj = -7.382276, rho = -0.129476
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 205
nu = 0.234145
obj = -8.240922, rho = -0.078648
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.203501
obj = -9.197854, rho = -0.118868
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.177528
obj = -10.301953, rho = -0.170757
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.154747
obj = -11.580728, rho = -0.214826
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.138809
obj = -13.028321, rho = -0.239913
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.122204
obj = -14.522672, rho = -0.339200
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.106727
obj = -16.212681, rho = -0.380946
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.096606
obj = -18.109846, rho = -0.356114
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.086332
obj = -19.897284, rho = -0.326914
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.074528
obj = -21.610792, rho = -0.380648
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.067607
obj = -23.140114, rho = -0.672255
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.058452
obj = -24.003505, rho = -0.693757
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.048323
obj = -24.161567, rho = -0.670785
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.558849
obj = -3.881684, rho = -0.114940
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.500660
obj = -4.445737, rho = -0.096104
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.452500
obj = -5.107627, rho = -0.095717
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.408878
obj = -5.851891, rho = -0.105177
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.367567
obj = -6.689393, rho = -0.165814
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.329452
obj = -7.639722, rho = -0.182700
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.295297
obj = -8.732839, rho = -0.200977
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.259849
obj = -10.018876, rho = -0.204200
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.240564
obj = -11.500616, rho = -0.224355
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.215275
obj = -13.134353, rho = -0.132938
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.201433
obj = -14.882674, rho = -0.044451
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.177782
obj = -16.589535, rho = -0.026466
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.158385
obj = -18.463738, rho = 0.009413
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.138909
obj = -20.416137, rho = -0.041889
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.120252
obj = -22.604408, rho = -0.047005
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.106328
obj = -24.800146, rho = -0.122903
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.093921
obj = -27.027998, rho = -0.239447
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.081103
obj = -29.185864, rho = -0.116454
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.071169
obj = -31.072742, rho = -0.172376
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.062617
obj = -31.909131, rho = -0.264529
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.564577
obj = -3.837136, rho = 0.092871
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.515275
obj = -4.333328, rho = 0.081764
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.447844
obj = -4.888569, rho = 0.084034
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.397581
obj = -5.550134, rho = 0.118846
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.355240
obj = -6.299976, rho = 0.071482
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.323263
obj = -7.098358, rho = -0.023310
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.281810
obj = -7.970466, rho = -0.049868
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.242358
obj = -9.018225, rho = -0.064402
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.218903
obj = -10.215376, rho = -0.154158
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.194828
obj = -11.578401, rho = -0.206365
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.170498
obj = -13.134287, rho = -0.176189
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.154700
obj = -14.955990, rho = -0.111212
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.139602
obj = -16.864448, rho = -0.084742
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.124778
obj = -18.915620, rho = -0.063271
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.110760
obj = -21.168437, rho = -0.116338
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.097602
obj = -23.486745, rho = -0.114939
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.082627
obj = -26.190338, rho = -0.084231
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.072241
obj = -29.432271, rho = -0.088239
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.063285
obj = -33.193178, rho = -0.143986
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.055513
obj = -37.604974, rho = -0.227930
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.559270
obj = -3.786448, rho = -0.184682
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.499861
obj = -4.292855, rho = -0.217829
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.438179
obj = -4.876296, rho = -0.191381
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.395481
obj = -5.556971, rho = -0.127461
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.345991
obj = -6.338419, rho = -0.143403
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.314366
obj = -7.250170, rho = -0.213912
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.284205
obj = -8.214569, rho = -0.123698
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.250179
obj = -9.352956, rho = -0.144313
nSV = 27, nBSV = 24
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.238433
obj = -10.558632, rho = -0.286109
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.204613
obj = -11.736571, rho = -0.314088
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.175955
obj = -13.180141, rho = -0.350530
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 297
nu = 0.157748
obj = -14.838820, rho = -0.307748
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*..*
optimization finished, #iter = 228
nu = 0.136669
obj = -16.705202, rho = -0.284946
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.121740
obj = -18.869951, rho = -0.275060
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.113127
obj = -21.069304, rho = -0.305486
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.103910
obj = -22.887057, rho = -0.332459
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*.*
optimization finished, #iter = 308
nu = 0.088456
obj = -24.444578, rho = -0.368461
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 290
nu = 0.073243
obj = -26.043212, rho = -0.281717
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.061144
obj = -27.914501, rho = -0.340588
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.051536
obj = -29.727918, rho = -0.431735
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.567225
obj = -4.017965, rho = -0.130644
nSV = 58, nBSV = 54
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.509653
obj = -4.643387, rho = -0.108882
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.468132
obj = -5.364707, rho = -0.168860
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.421336
obj = -6.186247, rho = -0.169218
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.379333
obj = -7.143144, rho = -0.181936
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.347023
obj = -8.257901, rho = -0.104678
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.311670
obj = -9.548740, rho = -0.113527
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.289124
obj = -10.967139, rho = -0.035741
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.255244
obj = -12.613115, rho = -0.051571
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.233316
obj = -14.539755, rho = -0.036783
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.217386
obj = -16.630726, rho = -0.028018
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.192885
obj = -18.864547, rho = -0.005934
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.173460
obj = -21.411724, rho = -0.085057
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.160459
obj = -24.168440, rho = -0.086246
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.139663
obj = -26.883381, rho = -0.140711
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.119060
obj = -30.214218, rho = -0.172990
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.105334
obj = -34.098301, rho = -0.267811
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.097034
obj = -38.407804, rho = -0.483916
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.092508
obj = -41.848059, rho = -0.722014
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.081691
obj = -43.967960, rho = -0.880281
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.520000
obj = -3.494806, rho = -0.109883
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.468960
obj = -3.930444, rho = -0.064721
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.416754
obj = -4.402498, rho = -0.063656
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.364936
obj = -4.926103, rho = -0.038854
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.318458
obj = -5.520828, rho = -0.052381
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.284364
obj = -6.183104, rho = -0.063876
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.249320
obj = -6.910807, rho = -0.096783
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.214144
obj = -7.755500, rho = -0.121138
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.190083
obj = -8.735765, rho = -0.121529
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.168677
obj = -9.828487, rho = -0.154321
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.151492
obj = -10.976189, rho = -0.134271
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.129104
obj = -12.290389, rho = -0.148099
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.114465
obj = -13.862552, rho = -0.210063
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.100813
obj = -15.548588, rho = -0.213817
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 334
nu = 0.088681
obj = -17.543264, rho = -0.269401
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.079613
obj = -19.786795, rho = -0.289328
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.069818
obj = -22.192814, rho = -0.318724
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.064449
obj = -24.710097, rho = -0.296244
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.058440
obj = -26.734545, rho = -0.210896
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.050273
obj = -28.545999, rho = -0.228773
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.516675
obj = -3.341616, rho = -0.167204
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.449860
obj = -3.742257, rho = -0.133674
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.395885
obj = -4.182953, rho = -0.118760
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.341849
obj = -4.698365, rho = -0.115973
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.301230
obj = -5.302991, rho = -0.099944
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.263056
obj = -6.009335, rho = -0.090165
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.236333
obj = -6.798937, rho = -0.101340
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.206782
obj = -7.703356, rho = -0.070707
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.184509
obj = -8.771370, rho = -0.023798
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.163750
obj = -10.008448, rho = -0.043970
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.146475
obj = -11.436403, rho = -0.123988
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.131689
obj = -13.092881, rho = -0.198664
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.121441
obj = -14.927267, rho = -0.285646
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.105559
obj = -16.919858, rho = -0.275483
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.093394
obj = -19.354590, rho = -0.220568
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.084266
obj = -22.211801, rho = -0.150118
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.074240
obj = -25.572241, rho = -0.182168
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.068847
obj = -29.429670, rho = -0.074895
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.062519
obj = -33.544837, rho = 0.072159
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.055921
obj = -38.226428, rho = 0.215000
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.577883
obj = -3.944414, rho = -0.169874
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.530151
obj = -4.467934, rho = -0.173803
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.465593
obj = -5.031358, rho = -0.158149
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.406856
obj = -5.695787, rho = -0.154053
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.363619
obj = -6.445549, rho = -0.167560
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.319499
obj = -7.320680, rho = -0.171183
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.282053
obj = -8.363344, rho = -0.171929
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.259136
obj = -9.547947, rho = -0.147455
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.227152
obj = -10.867848, rho = -0.148964
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.201292
obj = -12.426684, rho = -0.168752
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 188
nu = 0.176363
obj = -14.332706, rho = -0.173400
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.160431
obj = -16.617339, rho = -0.281662
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.144078
obj = -19.326439, rho = -0.303716
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.136118
obj = -22.452868, rho = -0.501185
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.122614
obj = -25.796285, rho = -0.557707
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.115685
obj = -29.549404, rho = -0.728786
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*..*
optimization finished, #iter = 215
nu = 0.104932
obj = -33.253356, rho = -0.799889
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.095532
obj = -37.171412, rho = -0.845336
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.085826
obj = -40.881500, rho = -0.821987
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.076830
obj = -44.053367, rho = -0.818357
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.488801
obj = -3.197213, rho = -0.050272
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.441189
obj = -3.558510, rho = -0.071486
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.380269
obj = -3.943214, rho = -0.071597
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.334643
obj = -4.381998, rho = -0.047160
nSV = 34, nBSV = 31
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.287959
obj = -4.849252, rho = -0.057302
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.259992
obj = -5.369541, rho = -0.150894
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.228097
obj = -5.858477, rho = -0.084836
nSV = 25, nBSV = 21
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.195446
obj = -6.338675, rho = -0.102829
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.172093
obj = -6.766419, rho = -0.188799
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.143645
obj = -7.143350, rho = -0.228173
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.123788
obj = -7.490397, rho = -0.219105
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.101085
obj = -7.707571, rho = -0.190322
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.082587
obj = -7.911057, rho = -0.213113
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.067526
obj = -8.049381, rho = -0.199823
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.054262
obj = -8.074648, rho = -0.237929
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.042579
obj = -8.074650, rho = -0.237469
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.033415
obj = -8.074650, rho = -0.237469
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.026222
obj = -8.074650, rho = -0.237469
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.020578
obj = -8.074650, rho = -0.237469
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.016149
obj = -8.074650, rho = -0.237469
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.552156
obj = -3.622625, rho = -0.166264
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.483129
obj = -4.068995, rho = -0.127276
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.432196
obj = -4.573273, rho = -0.160490
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.380795
obj = -5.119565, rho = -0.213969
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.333370
obj = -5.710940, rho = -0.230551
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.285270
obj = -6.420450, rho = -0.233964
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.256158
obj = -7.254091, rho = -0.241515
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.228306
obj = -8.135265, rho = -0.195153
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.205393
obj = -9.046076, rho = -0.152525
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.176410
obj = -10.036360, rho = -0.153918
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.151068
obj = -11.218856, rho = -0.152016
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 168
nu = 0.131978
obj = -12.629096, rho = -0.215468
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.114639
obj = -14.296978, rho = -0.211906
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.102600
obj = -16.243162, rho = -0.111894
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.092095
obj = -18.481512, rho = -0.045607
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.083864
obj = -20.856799, rho = -0.006765
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.078545
obj = -23.106010, rho = 0.104207
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.067361
obj = -25.111970, rho = 0.141447
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.058347
obj = -27.247699, rho = 0.179555
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*..*
optimization finished, #iter = 276
nu = 0.049663
obj = -29.344983, rho = 0.242656
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.509503
obj = -3.392586, rho = -0.167057
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.454515
obj = -3.813285, rho = -0.169409
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.410110
obj = -4.247621, rho = -0.092921
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.363890
obj = -4.706117, rho = -0.073372
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.313495
obj = -5.197966, rho = -0.082502
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.274392
obj = -5.737456, rho = -0.032252
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.237845
obj = -6.290825, rho = 0.001313
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.202925
obj = -6.914156, rho = 0.022006
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.171409
obj = -7.661810, rho = 0.014296
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.152829
obj = -8.503827, rho = -0.087449
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.133355
obj = -9.330724, rho = -0.119518
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.115301
obj = -10.278589, rho = -0.151616
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.101541
obj = -11.143601, rho = -0.270799
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.088169
obj = -12.037177, rho = -0.275204
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.074057
obj = -12.917619, rho = -0.243542
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 257
nu = 0.061058
obj = -13.956013, rho = -0.233244
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.051788
obj = -15.204332, rho = -0.217709
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.045160
obj = -16.502570, rho = -0.214794
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.040052
obj = -17.629858, rho = -0.259756
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.034670
obj = -18.378682, rho = -0.146765
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.606671
obj = -4.197881, rho = -0.034620
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.544480
obj = -4.804489, rho = 0.003159
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.493589
obj = -5.475377, rho = 0.046623
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.441717
obj = -6.250631, rho = 0.029289
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.394289
obj = -7.111744, rho = 0.009814
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.356825
obj = -8.109625, rho = 0.006501
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.329018
obj = -9.145105, rho = 0.093306
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.281356
obj = -10.282260, rho = 0.096367
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.250897
obj = -11.637210, rho = 0.143832
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.222859
obj = -13.181337, rho = 0.083601
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.195873
obj = -14.877695, rho = 0.100997
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.174975
obj = -16.799887, rho = 0.078386
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.157187
obj = -18.910599, rho = 0.131782
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.135737
obj = -21.267606, rho = 0.120779
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.118877
obj = -24.216059, rho = 0.110467
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.108632
obj = -27.440812, rho = 0.054993
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.098739
obj = -30.745166, rho = 0.010966
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.089859
obj = -34.064317, rho = 0.277603
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.077745
obj = -37.111237, rho = 0.483738
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.067307
obj = -40.139804, rho = 0.569641
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.502006
obj = -3.573225, rho = -0.389775
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.456403
obj = -4.134049, rho = -0.383318
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.415136
obj = -4.776986, rho = -0.370375
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.380000
obj = -5.511721, rho = -0.371055
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.349866
obj = -6.294852, rho = -0.290632
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.310618
obj = -7.190486, rho = -0.295518
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.280890
obj = -8.193210, rho = -0.339336
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.248054
obj = -9.329334, rho = -0.456335
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.224095
obj = -10.620702, rho = -0.611657
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.196205
obj = -12.149492, rho = -0.587890
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.177002
obj = -13.911772, rho = -0.711201
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.160000
obj = -15.987103, rho = -0.664359
nSV = 18, nBSV = 14
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.150092
obj = -18.082082, rho = -0.510920
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.137753
obj = -20.154747, rho = -0.504761
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.116520
obj = -22.322689, rho = -0.524828
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.099243
obj = -25.023320, rho = -0.532566
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.087905
obj = -28.253623, rho = -0.454676
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.075972
obj = -32.005171, rho = -0.471609
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.069830
obj = -36.344909, rho = -0.539004
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.067602
obj = -40.281269, rho = -0.735984
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.600534
obj = -4.145695, rho = -0.049723
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.537909
obj = -4.740401, rho = -0.001402
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.491504
obj = -5.394628, rho = -0.085709
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 95% (95/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.439521
obj = -6.116285, rho = -0.036599
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 95% (95/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.389447
obj = -6.934099, rho = 0.011096
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.338128
obj = -7.898762, rho = 0.031103
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.299859
obj = -9.080935, rho = -0.026181
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.272391
obj = -10.458475, rho = 0.050679
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.244455
obj = -12.044407, rho = 0.031955
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.215593
obj = -13.962140, rho = 0.044981
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.196739
obj = -16.299417, rho = 0.051148
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.182324
obj = -18.990038, rho = 0.059801
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.164796
obj = -22.097622, rho = -0.032539
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.147771
obj = -25.829092, rho = -0.106698
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.133034
obj = -30.476089, rho = -0.139989
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.125993
obj = -36.091163, rho = -0.207138
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.121144
obj = -42.168541, rho = -0.325038
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*...*
optimization finished, #iter = 762
nu = 0.108028
obj = -48.995877, rho = -0.349401
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
....*..*
optimization finished, #iter = 687
nu = 0.096047
obj = -57.553433, rho = -0.341647
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.089248
obj = -68.260055, rho = -0.341488
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.593825
obj = -4.167065, rho = -0.064975
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.540786
obj = -4.776514, rho = -0.031725
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.483867
obj = -5.479059, rho = -0.030400
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.436748
obj = -6.295220, rho = -0.055762
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.396994
obj = -7.216674, rho = -0.040782
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.354115
obj = -8.239346, rho = -0.034120
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.317421
obj = -9.419307, rho = -0.080793
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.283819
obj = -10.812235, rho = -0.104767
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.254545
obj = -12.421223, rho = -0.154203
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*..*
optimization finished, #iter = 205
nu = 0.228460
obj = -14.315994, rho = -0.205158
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.206988
obj = -16.503850, rho = -0.211506
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...*
optimization finished, #iter = 454
nu = 0.185109
obj = -19.047714, rho = -0.164662
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.163993
obj = -22.180552, rho = -0.197586
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.148320
obj = -25.982976, rho = -0.273546
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.136838
obj = -30.599140, rho = -0.261755
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.127396
obj = -35.881908, rho = -0.299219
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.115127
obj = -42.105271, rho = -0.331392
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.106155
obj = -49.631004, rho = -0.463275
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.101162
obj = -58.281266, rho = -0.645661
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.092816
obj = -68.026782, rho = -0.766113
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.555064
obj = -3.794189, rho = -0.324144
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.493569
obj = -4.316449, rho = -0.314063
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.447795
obj = -4.920352, rho = -0.313741
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.401096
obj = -5.575150, rho = -0.313536
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.357608
obj = -6.284617, rho = -0.305501
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.314968
obj = -7.120497, rho = -0.281743
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.277316
obj = -8.083931, rho = -0.254448
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.250250
obj = -9.177571, rho = -0.171936
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.223046
obj = -10.384448, rho = -0.234731
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.197144
obj = -11.759335, rho = -0.225005
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.182279
obj = -13.235635, rho = -0.121529
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.160455
obj = -14.684702, rho = -0.063186
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.140936
obj = -16.285994, rho = -0.031600
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.120849
obj = -18.059214, rho = -0.017885
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.109525
obj = -19.805941, rho = -0.124859
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*...*
optimization finished, #iter = 414
nu = 0.094797
obj = -21.562237, rho = -0.241027
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.079858
obj = -23.429055, rho = -0.254974
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.069305
obj = -25.404565, rho = -0.359892
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.058990
obj = -27.534628, rho = -0.387463
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.053332
obj = -29.319908, rho = -0.374477
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.550332
obj = -3.743954, rho = -0.130319
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.494352
obj = -4.262169, rho = -0.124090
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.443365
obj = -4.836255, rho = -0.142308
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.405395
obj = -5.426520, rho = -0.133300
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.361815
obj = -6.056621, rho = -0.122989
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.327402
obj = -6.655911, rho = -0.176498
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.281697
obj = -7.196647, rho = -0.167611
nSV = 33, nBSV = 23
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.**..*
optimization finished, #iter = 362
nu = 0.235887
obj = -7.822479, rho = -0.159143
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 226
nu = 0.202180
obj = -8.531424, rho = -0.122609
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.174597
obj = -9.256931, rho = -0.150436
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.149087
obj = -9.992088, rho = -0.176353
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.125497
obj = -10.818406, rho = -0.169640
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.111106
obj = -11.572390, rho = -0.144930
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*.*
optimization finished, #iter = 172
nu = 0.094169
obj = -12.205543, rho = -0.167261
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*......*
optimization finished, #iter = 797
nu = 0.077041
obj = -12.810899, rho = -0.164838
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 211
nu = 0.063300
obj = -13.527568, rho = -0.167226
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.052106
obj = -14.306482, rho = -0.161761
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.044629
obj = -15.023847, rho = -0.062307
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.036848
obj = -15.581759, rho = -0.010563
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*...*
optimization finished, #iter = 736
nu = 0.030629
obj = -16.006712, rho = 0.023885
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.557124
obj = -3.691643, rho = 0.141454
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.491420
obj = -4.157995, rho = 0.202285
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.437837
obj = -4.686251, rho = 0.190961
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.381500
obj = -5.287544, rho = 0.191640
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.333152
obj = -5.995792, rho = 0.209431
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.300000
obj = -6.833938, rho = 0.125437
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.268469
obj = -7.737472, rho = 0.040927
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.239732
obj = -8.771143, rho = 0.127376
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.210163
obj = -9.933067, rho = 0.236391
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.189221
obj = -11.310702, rho = 0.242955
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.167163
obj = -12.848812, rho = 0.292740
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.149566
obj = -14.598483, rho = 0.246529
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.132988
obj = -16.610333, rho = 0.190098
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.118267
obj = -18.927039, rho = 0.177383
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.104945
obj = -21.617395, rho = 0.217222
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.092593
obj = -24.806930, rho = 0.226074
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.082064
obj = -28.707044, rho = 0.183513
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.075182
obj = -33.398497, rho = 0.127374
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.069379
obj = -38.618550, rho = 0.081051
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.064053
obj = -44.411427, rho = -0.004213
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.563601
obj = -3.737545, rho = -0.220384
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.497939
obj = -4.206110, rho = -0.226753
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.453455
obj = -4.714860, rho = -0.168083
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.405242
obj = -5.219673, rho = -0.144214
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.346638
obj = -5.753767, rho = -0.127897
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.303447
obj = -6.340218, rho = -0.149220
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.265717
obj = -6.932005, rho = -0.226223
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.226830
obj = -7.533642, rho = -0.256616
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.194899
obj = -8.212087, rho = -0.259428
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.169140
obj = -8.901784, rho = -0.243623
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.145159
obj = -9.605884, rho = -0.248243
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.123280
obj = -10.335138, rho = -0.241629
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*...*
optimization finished, #iter = 426
nu = 0.104989
obj = -11.018354, rho = -0.281799
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.087889
obj = -11.711470, rho = -0.295633
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*...*
optimization finished, #iter = 569
nu = 0.072003
obj = -12.472129, rho = -0.294583
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.**..*
optimization finished, #iter = 304
nu = 0.059606
obj = -13.419140, rho = -0.297938
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 206
nu = 0.051743
obj = -14.340491, rho = -0.316597
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.044458
obj = -15.139536, rho = -0.253770
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.038092
obj = -15.705921, rho = -0.232306
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.031597
obj = -15.797927, rho = -0.192431
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.570264
obj = -3.911676, rho = -0.190591
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.523390
obj = -4.440917, rho = -0.230854
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.468988
obj = -5.011586, rho = -0.256072
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.410600
obj = -5.631614, rho = -0.286544
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.363603
obj = -6.363777, rho = -0.259878
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.321567
obj = -7.164780, rho = -0.187819
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.283611
obj = -8.093708, rho = -0.201487
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.248473
obj = -9.130353, rho = -0.186533
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.217258
obj = -10.396038, rho = -0.148248
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.193216
obj = -11.893869, rho = -0.151638
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.180235
obj = -13.550191, rho = -0.160088
nSV = 20, nBSV = 16
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.158891
obj = -15.265201, rho = -0.142322
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.141324
obj = -17.182550, rho = -0.062485
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.123602
obj = -19.463024, rho = -0.045606
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.110057
obj = -22.122298, rho = -0.166287
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.095356
obj = -25.221951, rho = -0.152591
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.085368
obj = -28.986335, rho = 0.016640
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.078160
obj = -33.313473, rho = 0.100162
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.073343
obj = -37.824255, rho = 0.208232
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*....*
optimization finished, #iter = 711
nu = 0.068385
obj = -41.761536, rho = 0.229327
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.569914
obj = -3.926804, rho = -0.153575
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.508820
obj = -4.490373, rho = -0.130984
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.459875
obj = -5.124768, rho = -0.135965
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.415208
obj = -5.845741, rho = -0.123771
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.369005
obj = -6.645057, rho = -0.117668
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.333339
obj = -7.542697, rho = -0.180100
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.300659
obj = -8.493340, rho = -0.203061
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.259857
obj = -9.601361, rho = -0.178457
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.234328
obj = -10.918765, rho = -0.292116
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.212505
obj = -12.243079, rho = -0.453652
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.187271
obj = -13.710288, rho = -0.509071
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.170052
obj = -15.176844, rho = -0.499757
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.149276
obj = -16.582987, rho = -0.511908
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.129020
obj = -18.039155, rho = -0.507658
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*..*
optimization finished, #iter = 270
nu = 0.107391
obj = -19.636227, rho = -0.525802
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.096577
obj = -21.417615, rho = -0.483966
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.*
optimization finished, #iter = 484
nu = 0.083584
obj = -22.616377, rho = -0.518842
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.*
optimization finished, #iter = 411
nu = 0.069315
obj = -23.866904, rho = -0.521178
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.058122
obj = -24.996312, rho = -0.532819
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*....*
optimization finished, #iter = 455
nu = 0.047803
obj = -25.892157, rho = -0.505256
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.557894
obj = -3.813706, rho = -0.018627
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.500000
obj = -4.352733, rho = 0.030746
nSV = 51, nBSV = 49
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.439596
obj = -4.962019, rho = 0.043947
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.389394
obj = -5.709007, rho = 0.088846
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.348989
obj = -6.585346, rho = 0.092592
nSV = 40, nBSV = 31
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.320000
obj = -7.633713, rho = 0.023517
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.286980
obj = -8.803100, rho = 0.074389
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.257650
obj = -10.223248, rho = 0.091264
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.232676
obj = -11.922160, rho = 0.074960
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.209963
obj = -13.951041, rho = 0.068217
nSV = 28, nBSV = 17
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.192783
obj = -16.433615, rho = -0.026654
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.179343
obj = -19.326653, rho = -0.160892
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.164614
obj = -22.719222, rho = -0.119197
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.154491
obj = -26.647679, rho = -0.186525
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.140091
obj = -31.281538, rho = -0.166686
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.131388
obj = -36.716325, rho = -0.233245
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.119262
obj = -42.918296, rho = -0.354272
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*..*
optimization finished, #iter = 323
nu = 0.108078
obj = -50.468782, rho = -0.485593
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*.*
optimization finished, #iter = 315
nu = 0.100670
obj = -59.579777, rho = -0.638167
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
...*
optimization finished, #iter = 378
nu = 0.093507
obj = -69.992406, rho = -0.625552
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.594075
obj = -3.932107, rho = -0.203632
nSV = 61, nBSV = 58
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.530456
obj = -4.402711, rho = -0.212716
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.458403
obj = -4.939800, rho = -0.201436
nSV = 51, nBSV = 41
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.404274
obj = -5.572714, rho = -0.196092
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.356381
obj = -6.292945, rho = -0.235429
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.318396
obj = -7.097498, rho = -0.305125
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.285560
obj = -7.966894, rho = -0.244756
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.255459
obj = -8.890216, rho = -0.235559
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.222609
obj = -9.881479, rho = -0.239394
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*........*
optimization finished, #iter = 875
nu = 0.192060
obj = -11.005317, rho = -0.215405
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.176348
obj = -12.239947, rho = -0.179289
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.152825
obj = -13.315437, rho = -0.179524
nSV = 22, nBSV = 10
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.128109
obj = -14.557650, rho = -0.166870
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.109762
obj = -16.024007, rho = -0.171202
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.097757
obj = -17.516948, rho = -0.065410
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.085087
obj = -18.987011, rho = -0.116255
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.073055
obj = -20.279197, rho = -0.095293
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.061228
obj = -21.585952, rho = -0.137747
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.054021
obj = -22.610580, rho = -0.191636
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.045724
obj = -22.865139, rho = -0.218465
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.553785
obj = -3.729135, rho = -0.114417
nSV = 60, nBSV = 52
Total nSV = 60
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.488309
obj = -4.236873, rho = -0.124649
nSV = 51, nBSV = 48
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.434573
obj = -4.809676, rho = -0.155422
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.382914
obj = -5.476776, rho = -0.144247
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.351344
obj = -6.246087, rho = -0.186326
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.306335
obj = -7.115806, rho = -0.157572
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.275081
obj = -8.128218, rho = -0.115830
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.249061
obj = -9.289826, rho = -0.070512
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.231554
obj = -10.490637, rho = 0.061776
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.203478
obj = -11.740795, rho = 0.108680
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.179799
obj = -13.148721, rho = 0.183726
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.153368
obj = -14.757099, rho = 0.200533
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.135350
obj = -16.713758, rho = 0.271515
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.119595
obj = -19.017039, rho = 0.255612
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.107777
obj = -21.526304, rho = 0.284781
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*.*
optimization finished, #iter = 451
nu = 0.094076
obj = -24.404361, rho = 0.330154
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 282
nu = 0.083704
obj = -27.918389, rho = 0.449830
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.073854
obj = -32.022592, rho = 0.502218
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.068888
obj = -36.686418, rho = 0.742259
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.059813
obj = -41.935359, rho = 0.765440
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.595728
obj = -4.190301, rho = -0.220492
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.543496
obj = -4.801175, rho = -0.283962
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.498255
obj = -5.477752, rho = -0.288558
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.440137
obj = -6.218283, rho = -0.289597
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.392161
obj = -7.080092, rho = -0.223196
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.350752
obj = -8.076064, rho = -0.213266
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.315068
obj = -9.203350, rho = -0.194001
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.282242
obj = -10.458795, rho = -0.180823
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.253760
obj = -11.858466, rho = -0.097020
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.224923
obj = -13.451673, rho = -0.080157
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.200567
obj = -15.221594, rho = -0.092670
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.178136
obj = -17.279279, rho = -0.050538
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.161180
obj = -19.485410, rho = -0.057604
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.142198
obj = -21.981252, rho = -0.083810
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.125294
obj = -24.755161, rho = -0.217685
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.111040
obj = -27.874881, rho = -0.376359
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.098975
obj = -31.366673, rho = -0.414586
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.088530
obj = -34.927628, rho = -0.445633
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.079036
obj = -38.668656, rho = -0.476137
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.071100
obj = -42.469604, rho = -0.482314
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.562608
obj = -3.743884, rho = -0.067327
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.490697
obj = -4.240462, rho = -0.039582
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.438526
obj = -4.824615, rho = -0.074738
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.392793
obj = -5.466906, rho = -0.089320
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.352134
obj = -6.167222, rho = -0.064206
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.308296
obj = -6.983945, rho = -0.120777
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.277826
obj = -7.870571, rho = -0.096120
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.248198
obj = -8.849222, rho = -0.026689
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.215333
obj = -9.951003, rho = -0.013983
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.191608
obj = -11.184173, rho = -0.015415
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.170389
obj = -12.553666, rho = -0.089083
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.151105
obj = -14.071969, rho = -0.050347
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.134799
obj = -15.660764, rho = 0.007534
nSV = 15, nBSV = 11
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.121287
obj = -17.136304, rho = 0.045618
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.105340
obj = -18.563643, rho = -0.027129
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.087723
obj = -20.088543, rho = -0.020978
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.073548
obj = -21.930568, rho = 0.070237
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.063451
obj = -24.115042, rho = 0.223032
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.058413
obj = -26.021732, rho = 0.429711
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.051667
obj = -26.827016, rho = 0.497942
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.565112
obj = -3.814692, rho = -0.269307
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.501692
obj = -4.326617, rho = -0.250298
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.449463
obj = -4.908697, rho = -0.254315
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.409820
obj = -5.508662, rho = -0.257059
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.354875
obj = -6.185338, rho = -0.251963
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.309395
obj = -6.996124, rho = -0.286314
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.273038
obj = -7.933090, rho = -0.326649
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.243075
obj = -9.017585, rho = -0.270789
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.213408
obj = -10.284100, rho = -0.254055
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.189659
obj = -11.831616, rho = -0.211881
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.175157
obj = -13.568607, rho = -0.229836
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.160959
obj = -15.330968, rho = -0.246369
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.142139
obj = -17.312072, rho = -0.258503
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.124985
obj = -19.551034, rho = -0.319935
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.111612
obj = -22.094123, rho = -0.460829
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.100364
obj = -24.715846, rho = -0.622412
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.086644
obj = -27.797634, rho = -0.671080
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.080000
obj = -31.088502, rho = -0.850814
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.075483
obj = -33.590473, rho = -1.115873
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.064629
obj = -35.324006, rho = -1.142327
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.619498
obj = -4.424323, rho = -0.314347
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 95% (95/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.558740
obj = -5.127464, rho = -0.320809
nSV = 60, nBSV = 53
Total nSV = 60
Accuracy = 95% (95/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.505359
obj = -5.949090, rho = -0.313535
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 96% (96/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.460729
obj = -6.890944, rho = -0.305240
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.421413
obj = -7.990884, rho = -0.255246
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.382057
obj = -9.276795, rho = -0.227521
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.345874
obj = -10.786282, rho = -0.222227
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.317589
obj = -12.568349, rho = -0.202453
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.289555
obj = -14.596618, rho = -0.199732
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.266355
obj = -16.937336, rho = -0.203793
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 223
nu = 0.248461
obj = -19.548864, rho = -0.186010
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.229608
obj = -22.309553, rho = -0.228634
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.205234
obj = -25.187883, rho = -0.287020
nSV = 25, nBSV = 14
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.176415
obj = -28.678812, rho = -0.289321
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.156730
obj = -33.039994, rho = -0.339760
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.143784
obj = -37.999789, rho = -0.461097
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.133667
obj = -43.329486, rho = -0.530929
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.118575
obj = -48.994494, rho = -0.530274
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 245
nu = 0.105563
obj = -55.484509, rho = -0.494499
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*..*
optimization finished, #iter = 297
nu = 0.095155
obj = -62.535765, rho = -0.392840
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.511872
obj = -3.380814, rho = -0.183850
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.447940
obj = -3.803449, rho = -0.221464
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.391152
obj = -4.308021, rho = -0.255734
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.351833
obj = -4.892714, rho = -0.254821
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.313126
obj = -5.547430, rho = -0.297339
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.276028
obj = -6.296980, rho = -0.288017
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.251592
obj = -7.108101, rho = -0.305631
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.217644
obj = -8.009953, rho = -0.330304
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.193228
obj = -9.104455, rho = -0.339422
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.173011
obj = -10.318169, rho = -0.355780
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.158837
obj = -11.610447, rho = -0.338267
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.141310
obj = -12.871064, rho = -0.314617
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.124866
obj = -14.281165, rho = -0.251611
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.108826
obj = -15.675452, rho = -0.273124
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.092459
obj = -17.253458, rho = -0.346952
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.081304
obj = -18.923626, rho = -0.410859
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.068589
obj = -20.754486, rho = -0.365335
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.058886
obj = -22.977186, rho = -0.387741
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.052071
obj = -25.369040, rho = -0.458445
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.046483
obj = -27.663493, rho = -0.421272
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.523434
obj = -3.609032, rho = -0.061150
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.475953
obj = -4.110218, rho = -0.025719
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.422849
obj = -4.662059, rho = -0.000408
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.378411
obj = -5.298749, rho = -0.024365
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.345392
obj = -5.992253, rho = 0.050701
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.311913
obj = -6.705341, rho = 0.082758
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.278822
obj = -7.411139, rho = 0.111274
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.245675
obj = -8.072636, rho = 0.147487
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.206909
obj = -8.771453, rho = 0.148850
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.175732
obj = -9.577751, rho = 0.138424
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.151292
obj = -10.487712, rho = 0.129464
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.128302
obj = -11.499724, rho = 0.050531
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.113040
obj = -12.566674, rho = 0.044700
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.095875
obj = -13.728922, rho = 0.059482
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.081685
obj = -15.017230, rho = 0.136629
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.072125
obj = -16.260966, rho = 0.125759
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.060429
obj = -17.617941, rho = 0.144088
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.051203
obj = -19.175308, rho = 0.176225
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.044520
obj = -20.852429, rho = 0.419139
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.038163
obj = -22.444558, rho = 0.549802
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.612824
obj = -4.192241, rho = -0.124144
nSV = 65, nBSV = 58
Total nSV = 65
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.545579
obj = -4.778119, rho = -0.084866
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.487397
obj = -5.461630, rho = -0.112422
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.440000
obj = -6.236784, rho = -0.087968
nSV = 45, nBSV = 42
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.399946
obj = -7.079066, rho = -0.097880
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.357126
obj = -8.006077, rho = -0.109839
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.314856
obj = -9.041857, rho = -0.096773
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.281204
obj = -10.226164, rho = -0.127388
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.245298
obj = -11.577851, rho = -0.100898
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.220220
obj = -13.103354, rho = -0.176839
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.198734
obj = -14.750231, rho = -0.032948
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.175005
obj = -16.613843, rho = -0.094573
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.156651
obj = -18.586578, rho = -0.167591
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.141506
obj = -20.730025, rho = -0.040892
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.133059
obj = -22.373451, rho = 0.006947
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.112106
obj = -23.380362, rho = 0.024696
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.092026
obj = -24.476071, rho = -0.009651
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.078117
obj = -25.221850, rho = -0.036292
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.062913
obj = -25.772475, rho = -0.038502
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.052175
obj = -26.088863, rho = -0.131386
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.514063
obj = -3.570960, rho = -0.093095
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.477988
obj = -4.066125, rho = -0.099324
nSV = 48, nBSV = 45
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.427062
obj = -4.581675, rho = -0.105065
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.373911
obj = -5.161563, rho = -0.141555
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.333899
obj = -5.829211, rho = -0.111560
nSV = 35, nBSV = 32
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.298920
obj = -6.548098, rho = -0.105806
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.263544
obj = -7.312639, rho = -0.054093
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.233246
obj = -8.140335, rho = -0.030740
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.201066
obj = -9.072098, rho = -0.050840
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.176907
obj = -10.138851, rho = -0.024658
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.157072
obj = -11.235669, rho = 0.014042
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.135375
obj = -12.460937, rho = -0.005372
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.117757
obj = -13.841115, rho = -0.048941
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.103183
obj = -15.445744, rho = 0.031130
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.091623
obj = -17.095034, rho = 0.092038
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.081438
obj = -18.766882, rho = 0.129606
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.072456
obj = -20.203709, rho = 0.149275
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.060918
obj = -21.498198, rho = 0.253836
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.051178
obj = -22.844947, rho = 0.399461
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.043153
obj = -24.113537, rho = 0.554276
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.556010
obj = -3.636193, rho = -0.180173
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.496185
obj = -4.054403, rho = -0.220046
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.436107
obj = -4.499720, rho = -0.234554
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.372619
obj = -5.007282, rho = -0.250042
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.333281
obj = -5.552176, rho = -0.204515
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.288805
obj = -6.151837, rho = -0.196857
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.247947
obj = -6.834417, rho = -0.179237
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.224691
obj = -7.574289, rho = -0.116423
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.193712
obj = -8.297722, rho = -0.127384
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.168825
obj = -9.083194, rho = -0.166241
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.142815
obj = -9.908267, rho = -0.161797
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.125224
obj = -10.784421, rho = -0.025021
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.107394
obj = -11.661813, rho = 0.003064
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.091495
obj = -12.569580, rho = 0.002964
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.077100
obj = -13.551626, rho = -0.040296
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.066522
obj = -14.517579, rho = -0.039376
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.056239
obj = -15.413529, rho = -0.020294
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.048785
obj = -16.053037, rho = 0.075419
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.041426
obj = -16.300394, rho = 0.185934
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 318
nu = 0.032599
obj = -16.301623, rho = 0.178321
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.591149
obj = -3.867382, rho = -0.021154
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.523721
obj = -4.324568, rho = -0.004828
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.461159
obj = -4.827086, rho = -0.055773
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.406101
obj = -5.376766, rho = -0.081655
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.355128
obj = -5.993506, rho = -0.095202
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.310769
obj = -6.641652, rho = -0.136776
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.272763
obj = -7.357645, rho = -0.072170
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.240394
obj = -8.102961, rho = -0.097316
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.206352
obj = -8.905843, rho = -0.142362
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.179623
obj = -9.737320, rho = -0.095857
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.152308
obj = -10.673631, rho = -0.114266
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 375
nu = 0.129216
obj = -11.747776, rho = -0.122762
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.112262
obj = -13.030527, rho = -0.139484
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.096395
obj = -14.475162, rho = -0.143865
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.086825
obj = -16.099900, rho = -0.164993
nSV = 11, nBSV = 6
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.078930
obj = -17.393311, rho = -0.231011
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*..*
optimization finished, #iter = 421
nu = 0.065890
obj = -18.561924, rho = -0.266519
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 295
nu = 0.054055
obj = -20.008198, rho = -0.266415
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.045344
obj = -21.831388, rho = -0.239774
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.040062
obj = -23.736601, rho = -0.162420
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.580123
obj = -3.981928, rho = -0.091532
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.523194
obj = -4.531940, rho = -0.120740
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.470424
obj = -5.156971, rho = -0.131780
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.423332
obj = -5.814837, rho = -0.080217
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.368819
obj = -6.572955, rho = -0.119460
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.336384
obj = -7.398599, rho = -0.221468
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.299627
obj = -8.285555, rho = -0.227123
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.263463
obj = -9.233263, rho = -0.220368
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.233136
obj = -10.245209, rho = -0.136088
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.199267
obj = -11.375666, rho = -0.125457
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.177526
obj = -12.653376, rho = -0.174140
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*....*
optimization finished, #iter = 502
nu = 0.153131
obj = -13.971503, rho = -0.218216
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.131935
obj = -15.532898, rho = -0.226096
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*....*
optimization finished, #iter = 476
nu = 0.112668
obj = -17.352778, rho = -0.238115
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.096335
obj = -19.651826, rho = -0.233205
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.086413
obj = -22.413588, rho = -0.108911
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.076851
obj = -25.631800, rho = -0.106151
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.070566
obj = -29.100619, rho = -0.102725
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.064534
obj = -32.729195, rho = -0.439257
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.057666
obj = -36.385120, rho = -0.544023
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.536861
obj = -3.618247, rho = -0.099761
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.480749
obj = -4.092757, rho = -0.075555
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.428217
obj = -4.623518, rho = -0.108945
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.381004
obj = -5.209242, rho = -0.134489
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.331954
obj = -5.883037, rho = -0.104539
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.297967
obj = -6.663105, rho = -0.139517
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.263910
obj = -7.504097, rho = -0.134660
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.233351
obj = -8.451200, rho = -0.082018
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.203574
obj = -9.563534, rho = -0.089232
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.184770
obj = -10.776290, rho = -0.010144
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.168212
obj = -12.020107, rho = -0.047183
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 257
nu = 0.143837
obj = -13.354546, rho = -0.043836
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.127527
obj = -14.867791, rho = 0.040068
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.108885
obj = -16.564306, rho = 0.053781
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.098702
obj = -18.424255, rho = 0.028380
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*..*
optimization finished, #iter = 441
nu = 0.089533
obj = -19.992445, rho = 0.059636
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
......*....*
optimization finished, #iter = 1000
nu = 0.075427
obj = -21.472824, rho = 0.067705
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*......*
optimization finished, #iter = 662
nu = 0.062077
obj = -23.251607, rho = 0.067611
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.051882
obj = -25.508874, rho = 0.075200
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.046148
obj = -28.090190, rho = 0.065881
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.580000
obj = -4.025667, rho = -0.164251
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.513702
obj = -4.618804, rho = -0.178086
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.460977
obj = -5.321064, rho = -0.142825
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.424970
obj = -6.128123, rho = -0.234816
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.379362
obj = -7.039474, rho = -0.308516
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.347926
obj = -8.084536, rho = -0.304777
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.313565
obj = -9.256447, rho = -0.266454
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.283009
obj = -10.581106, rho = -0.240610
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.260000
obj = -12.059465, rho = -0.172784
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.228324
obj = -13.646549, rho = -0.176088
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.206193
obj = -15.454234, rho = -0.190995
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.184973
obj = -17.315240, rho = -0.275795
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.162386
obj = -19.381639, rho = -0.270038
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.141811
obj = -21.696735, rho = -0.252491
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.122025
obj = -24.466345, rho = -0.217350
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.106125
obj = -27.860448, rho = -0.175561
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.093650
obj = -32.035293, rho = -0.155586
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.085571
obj = -36.915752, rho = -0.044239
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.079356
obj = -42.127315, rho = 0.042236
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*..*
optimization finished, #iter = 430
nu = 0.070244
obj = -47.769970, rho = 0.060780
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.538882
obj = -3.798254, rho = -0.266307
nSV = 54, nBSV = 52
Total nSV = 54
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.488405
obj = -4.362979, rho = -0.213204
nSV = 51, nBSV = 48
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.441325
obj = -5.003782, rho = -0.156687
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.400373
obj = -5.720144, rho = -0.208993
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.360560
obj = -6.531920, rho = -0.187516
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.321164
obj = -7.440124, rho = -0.076223
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.287723
obj = -8.506435, rho = -0.022618
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.258547
obj = -9.732265, rho = -0.013354
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.242942
obj = -11.051740, rho = 0.083961
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.212409
obj = -12.373317, rho = 0.106647
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.187636
obj = -13.918193, rho = 0.221637
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.169114
obj = -15.540753, rho = 0.407243
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.150997
obj = -17.214957, rho = 0.437644
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 326
nu = 0.133708
obj = -18.740569, rho = 0.456202
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.112428
obj = -20.414868, rho = 0.504208
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*..*
optimization finished, #iter = 326
nu = 0.098391
obj = -22.085525, rho = 0.613894
nSV = 16, nBSV = 4
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.083396
obj = -23.921368, rho = 0.656119
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.073514
obj = -25.440484, rho = 0.869607
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 265
nu = 0.063167
obj = -26.484245, rho = 1.069826
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
......*
optimization finished, #iter = 697
nu = 0.050530
obj = -27.421352, rho = 1.070847
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.499051
obj = -3.398643, rho = -0.110436
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.440650
obj = -3.874096, rho = -0.136525
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.393863
obj = -4.420160, rho = -0.186558
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.350249
obj = -5.062858, rho = -0.224822
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.316071
obj = -5.828155, rho = -0.175445
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.286154
obj = -6.679877, rho = -0.220514
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.254062
obj = -7.674682, rho = -0.150829
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.228523
obj = -8.852340, rho = -0.142400
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.206769
obj = -10.209284, rho = -0.134556
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.186121
obj = -11.830729, rho = -0.061595
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.176113
obj = -13.585572, rho = -0.052126
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.161513
obj = -15.373388, rho = -0.037617
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.143691
obj = -17.250608, rho = -0.081692
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.124646
obj = -19.431411, rho = -0.072770
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.111517
obj = -21.903759, rho = -0.049999
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.097594
obj = -24.645220, rho = -0.107321
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 391
nu = 0.087618
obj = -27.754685, rho = -0.115667
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*..*
optimization finished, #iter = 482
nu = 0.076156
obj = -31.234542, rho = -0.126475
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*..*
optimization finished, #iter = 439
nu = 0.066891
obj = -35.322062, rho = -0.160458
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.061803
obj = -39.767877, rho = -0.093057
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.537643
obj = -3.538450, rho = -0.263710
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.483813
obj = -3.965026, rho = -0.195956
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.420434
obj = -4.427827, rho = -0.160013
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.369472
obj = -4.935652, rho = -0.156102
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.331646
obj = -5.468347, rho = -0.148111
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.288942
obj = -6.003178, rho = -0.169789
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.250646
obj = -6.578112, rho = -0.223069
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.220478
obj = -7.154981, rho = -0.269858
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.186392
obj = -7.732402, rho = -0.281705
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.156934
obj = -8.399921, rho = -0.293441
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.135736
obj = -9.093827, rho = -0.326638
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..............*
optimization finished, #iter = 1406
nu = 0.115722
obj = -9.820919, rho = -0.315291
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.097014
obj = -10.609596, rho = -0.294880
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*..*
optimization finished, #iter = 252
nu = 0.081581
obj = -11.479384, rho = -0.273370
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.070840
obj = -12.432825, rho = -0.274478
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.062730
obj = -13.216024, rho = -0.207391
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.052490
obj = -13.877635, rho = -0.081150
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.045019
obj = -14.244003, rho = 0.007949
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.036391
obj = -14.278993, rho = 0.009167
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.028559
obj = -14.278993, rho = 0.009167
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.569677
obj = -3.957187, rho = 0.066468
nSV = 59, nBSV = 56
Total nSV = 59
Accuracy = 95% (95/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.509321
obj = -4.534183, rho = 0.053561
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.459028
obj = -5.209425, rho = 0.007078
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.410962
obj = -5.987282, rho = -0.031548
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.365026
obj = -6.903261, rho = -0.014745
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.328142
obj = -8.025965, rho = 0.026250
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.300000
obj = -9.339558, rho = 0.049825
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.272755
obj = -10.871726, rho = 0.052552
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.250958
obj = -12.649301, rho = 0.170021
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.232345
obj = -14.672041, rho = 0.083796
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.210659
obj = -16.903140, rho = 0.175817
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.189896
obj = -19.570037, rho = 0.225451
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.169254
obj = -22.757125, rho = 0.223586
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.161505
obj = -26.293667, rho = 0.326141
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.147807
obj = -30.044548, rho = 0.357007
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.134793
obj = -34.216759, rho = 0.208995
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.121168
obj = -38.349539, rho = 0.117632
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.108918
obj = -43.000414, rho = 0.221017
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.094466
obj = -48.013715, rho = 0.234992
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 301
nu = 0.087777
obj = -52.854403, rho = 0.327800
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.576189
obj = -3.818817, rho = -0.321357
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.503409
obj = -4.308205, rho = -0.345143
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.443808
obj = -4.887216, rho = -0.341395
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.395452
obj = -5.545226, rho = -0.267687
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.352987
obj = -6.285930, rho = -0.276113
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.313216
obj = -7.135999, rho = -0.295427
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.276267
obj = -8.111148, rho = -0.271025
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.250439
obj = -9.210160, rho = -0.275009
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.223027
obj = -10.460768, rho = -0.350431
nSV = 25, nBSV = 21
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.199436
obj = -11.792477, rho = -0.315343
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.171718
obj = -13.402215, rho = -0.315736
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.159699
obj = -15.278590, rho = -0.175994
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.144541
obj = -17.226589, rho = -0.380927
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.125361
obj = -19.296230, rho = -0.509028
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.108514
obj = -21.782709, rho = -0.577134
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.098107
obj = -24.640666, rho = -0.670776
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.089676
obj = -27.513243, rho = -0.782199
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.077000
obj = -30.491035, rho = -0.865649
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.065355
obj = -34.189367, rho = -0.879926
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.056218
obj = -38.843238, rho = -0.898004
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.574430
obj = -3.845759, rho = -0.178155
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.504386
obj = -4.360169, rho = -0.192771
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.444297
obj = -4.957118, rho = -0.223499
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.394715
obj = -5.660127, rho = -0.174370
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.354686
obj = -6.472221, rho = -0.197257
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.320000
obj = -7.430787, rho = -0.175151
nSV = 35, nBSV = 30
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.286997
obj = -8.460062, rho = -0.162305
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.253511
obj = -9.699661, rho = -0.161774
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.228225
obj = -11.145837, rho = -0.120705
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.204714
obj = -12.836926, rho = -0.125573
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.187298
obj = -14.712473, rho = -0.229668
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.176673
obj = -16.739408, rho = -0.109839
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.159353
obj = -18.727378, rho = -0.058848
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.136655
obj = -20.900590, rho = -0.076153
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*....*
optimization finished, #iter = 533
nu = 0.123520
obj = -23.258099, rho = -0.077952
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*.*
optimization finished, #iter = 347
nu = 0.106425
obj = -25.765122, rho = -0.004791
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.094587
obj = -28.475882, rho = 0.195817
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*
optimization finished, #iter = 364
nu = 0.083533
obj = -31.146658, rho = 0.438565
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.........*..*
optimization finished, #iter = 1112
nu = 0.069871
obj = -34.047209, rho = 0.457100
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
....*....*.*
optimization finished, #iter = 930
nu = 0.059461
obj = -37.465037, rho = 0.472146
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.578679
obj = -3.876789, rho = -0.433274
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.510370
obj = -4.391997, rho = -0.418131
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.459403
obj = -4.979394, rho = -0.419062
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.405938
obj = -5.639805, rho = -0.387805
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.364076
obj = -6.351829, rho = -0.391272
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.317626
obj = -7.161000, rho = -0.395919
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.293137
obj = -8.064644, rho = -0.436845
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 236
nu = 0.256475
obj = -8.936862, rho = -0.441338
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.220692
obj = -9.959001, rho = -0.412129
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.196180
obj = -11.106304, rho = -0.408619
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.170989
obj = -12.367789, rho = -0.413841
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.149628
obj = -13.711621, rho = -0.459768
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 254
nu = 0.129666
obj = -15.263875, rho = -0.517718
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*
optimization finished, #iter = 307
nu = 0.114113
obj = -16.933138, rho = -0.538124
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*......*
optimization finished, #iter = 637
nu = 0.098365
obj = -18.798525, rho = -0.566741
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 252
nu = 0.083600
obj = -21.069135, rho = -0.594742
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.073247
obj = -23.836538, rho = -0.669412
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.065001
obj = -27.051725, rho = -0.754083
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.059948
obj = -30.499314, rho = -0.878448
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.056124
obj = -33.565634, rho = -1.005003
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.549627
obj = -3.777147, rho = -0.085926
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.509698
obj = -4.280828, rho = -0.036631
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.444445
obj = -4.823331, rho = -0.027074
nSV = 48, nBSV = 40
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.398178
obj = -5.449614, rho = -0.005682
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.360334
obj = -6.100473, rho = -0.025613
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.314303
obj = -6.797581, rho = -0.054794
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.273697
obj = -7.569089, rho = -0.119399
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.244154
obj = -8.419293, rho = -0.174718
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.217034
obj = -9.268629, rho = -0.179159
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.189984
obj = -10.108786, rho = -0.052799
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.162410
obj = -10.960629, rho = -0.053963
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.136431
obj = -11.880762, rho = -0.055453
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.116583
obj = -12.960278, rho = -0.094590
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.100165
obj = -14.068423, rho = -0.140182
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.087079
obj = -15.234064, rho = -0.101670
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.072910
obj = -16.393191, rho = -0.088531
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.062586
obj = -17.671556, rho = -0.114438
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.053000
obj = -18.902940, rho = -0.146290
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.044632
obj = -20.162752, rho = -0.136561
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.036683
obj = -21.575300, rho = -0.151650
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.594729
obj = -4.009903, rho = -0.124777
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.525138
obj = -4.554705, rho = -0.105006
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.480000
obj = -5.171259, rho = -0.054197
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.424297
obj = -5.836173, rho = -0.087518
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.367533
obj = -6.613481, rho = -0.084837
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.326288
obj = -7.548072, rho = -0.028982
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.291349
obj = -8.604410, rho = 0.004057
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.266210
obj = -9.783888, rho = -0.064049
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.234954
obj = -11.097505, rho = -0.096187
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.211995
obj = -12.573169, rho = -0.209319
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.187378
obj = -14.212110, rho = -0.232986
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.166064
obj = -16.119546, rho = -0.158127
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.146207
obj = -18.341566, rho = -0.167431
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.130783
obj = -20.929987, rho = -0.177912
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.120323
obj = -23.706407, rho = -0.225027
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.106149
obj = -26.752095, rho = -0.116296
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.092721
obj = -30.239017, rho = -0.048399
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.081742
obj = -34.348406, rho = -0.015820
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.076713
obj = -38.784582, rho = 0.000489
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 292
nu = 0.067484
obj = -43.063200, rho = 0.164215
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.517383
obj = -3.521816, rho = -0.351605
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.467848
obj = -3.994377, rho = -0.305701
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.414721
obj = -4.519870, rho = -0.320263
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.366253
obj = -5.111770, rho = -0.306330
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.328009
obj = -5.790526, rho = -0.287761
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.289513
obj = -6.564211, rho = -0.327097
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.252126
obj = -7.468571, rho = -0.304393
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.225240
obj = -8.570959, rho = -0.296922
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.203248
obj = -9.853076, rho = -0.284754
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.181533
obj = -11.294873, rho = -0.297556
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.162584
obj = -13.001786, rho = -0.272995
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.149604
obj = -14.966937, rho = -0.232113
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 245
nu = 0.136703
obj = -17.084990, rho = -0.295935
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.120434
obj = -19.506453, rho = -0.306137
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.109456
obj = -22.200829, rho = -0.375690
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.095914
obj = -25.364339, rho = -0.375794
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.087816
obj = -29.094914, rho = -0.435050
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.082779
obj = -32.819615, rho = -0.575392
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
...*.*
optimization finished, #iter = 433
nu = 0.075821
obj = -36.276101, rho = -0.732572
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
.*.*
optimization finished, #iter = 274
nu = 0.064311
obj = -39.695357, rho = -0.731753
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.498331
obj = -3.425552, rho = -0.433278
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.457745
obj = -3.891643, rho = -0.405674
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.400000
obj = -4.409778, rho = -0.398122
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.364936
obj = -4.985902, rho = -0.342056
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.319873
obj = -5.617207, rho = -0.288572
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.290220
obj = -6.327028, rho = -0.312457
nSV = 31, nBSV = 27
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.254885
obj = -7.070066, rho = -0.385401
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.225637
obj = -7.870452, rho = -0.331667
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.196814
obj = -8.736400, rho = -0.339923
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.170166
obj = -9.729383, rho = -0.388652
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.146079
obj = -10.906028, rho = -0.433884
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.128859
obj = -12.282567, rho = -0.458904
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.112332
obj = -13.895287, rho = -0.482575
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.097120
obj = -15.852795, rho = -0.490379
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.087997
obj = -18.216215, rho = -0.548974
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 108
nu = 0.081725
obj = -20.755142, rho = -0.610235
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.075346
obj = -23.227588, rho = -0.665781
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.067571
obj = -25.646708, rho = -0.628145
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.060634
obj = -27.838673, rho = -0.639384
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.054973
obj = -29.397088, rho = -0.534093
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.547892
obj = -3.684602, rho = -0.336724
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.492174
obj = -4.166681, rho = -0.332487
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.426252
obj = -4.719598, rho = -0.324654
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.383718
obj = -5.368540, rho = -0.404730
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.336353
obj = -6.118994, rho = -0.422871
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.304883
obj = -6.982659, rho = -0.340581
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.272518
obj = -7.947212, rho = -0.253195
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.246243
obj = -9.000521, rho = -0.186429
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.218284
obj = -10.181932, rho = -0.183353
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.190336
obj = -11.554211, rho = -0.159797
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.167204
obj = -13.229909, rho = -0.208563
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.146604
obj = -15.303912, rho = -0.235704
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.131577
obj = -17.862327, rho = -0.310312
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.119873
obj = -20.916458, rho = -0.404754
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.109876
obj = -24.605071, rho = -0.481240
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.102665
obj = -28.893592, rho = -0.591849
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.099634
obj = -33.522208, rho = -0.781577
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.094567
obj = -37.836958, rho = -0.967423
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.086007
obj = -42.095501, rho = -1.179513
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*..*
optimization finished, #iter = 492
nu = 0.078490
obj = -45.608647, rho = -1.445440
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.564228
obj = -3.839366, rho = -0.091072
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.514640
obj = -4.338540, rho = -0.063794
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.466372
obj = -4.843006, rho = -0.045655
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.403627
obj = -5.404191, rho = -0.047491
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.355174
obj = -6.023687, rho = -0.092252
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.314981
obj = -6.712721, rho = -0.074308
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.270358
obj = -7.449614, rho = -0.052526
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.239073
obj = -8.305247, rho = -0.114079
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.205951
obj = -9.240627, rho = -0.138840
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.177084
obj = -10.352638, rho = -0.164155
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.153314
obj = -11.696004, rho = -0.172236
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 254
nu = 0.137636
obj = -13.240014, rho = -0.198782
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.122980
obj = -14.979354, rho = -0.180554
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.108472
obj = -16.897475, rho = -0.168149
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.094412
obj = -19.096110, rho = -0.149323
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.081697
obj = -21.842636, rho = -0.131859
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.072231
obj = -25.242581, rho = -0.168511
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.063936
obj = -29.490236, rho = -0.203423
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.058728
obj = -34.685602, rho = -0.319086
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.055101
obj = -40.671868, rho = -0.452960
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.613804
obj = -4.144025, rho = -0.159534
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.548938
obj = -4.692114, rho = -0.136599
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.482084
obj = -5.320383, rho = -0.138491
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.424484
obj = -6.070496, rho = -0.148947
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.381290
obj = -6.929664, rho = -0.112912
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.346372
obj = -7.873387, rho = -0.013514
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.314473
obj = -8.920790, rho = -0.076276
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.281080
obj = -9.981794, rho = -0.135980
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.248856
obj = -11.152276, rho = -0.189955
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.215302
obj = -12.457246, rho = -0.215852
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.188049
obj = -13.968918, rho = -0.251470
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.163214
obj = -15.730024, rho = -0.232408
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.143180
obj = -17.787011, rho = -0.276479
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.131237
obj = -20.128287, rho = -0.330598
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.118987
obj = -22.387557, rho = -0.446118
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*...*
optimization finished, #iter = 315
nu = 0.101759
obj = -24.903451, rho = -0.438211
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.087906
obj = -27.891099, rho = -0.427393
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.076892
obj = -31.357442, rho = -0.319193
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.067558
obj = -35.362302, rho = -0.249628
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.062016
obj = -39.679028, rho = -0.403309
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.603630
obj = -4.066132, rho = -0.142982
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.540683
obj = -4.589365, rho = -0.124470
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.478787
obj = -5.192574, rho = -0.109805
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.421053
obj = -5.876467, rho = -0.081659
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.378034
obj = -6.644752, rho = -0.110475
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.332628
obj = -7.495762, rho = -0.099686
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.289934
obj = -8.518650, rho = -0.117218
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.261998
obj = -9.713860, rho = -0.098968
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.233765
obj = -11.037320, rho = -0.145864
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.209183
obj = -12.532516, rho = -0.220545
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.188017
obj = -14.246257, rho = -0.251532
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.167932
obj = -16.062931, rho = -0.261816
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.145542
obj = -18.205961, rho = -0.209217
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.134118
obj = -20.587453, rho = -0.198540
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.116376
obj = -23.153768, rho = -0.262710
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.101957
obj = -26.218155, rho = -0.301504
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.088728
obj = -29.967580, rho = -0.315968
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.081981
obj = -34.289226, rho = -0.358164
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.076963
obj = -38.421186, rho = -0.551619
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.070690
obj = -42.072123, rho = -0.742265
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.496110
obj = -3.399580, rho = -0.008693
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.455385
obj = -3.859359, rho = -0.036192
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.405799
obj = -4.354551, rho = -0.057597
nSV = 42, nBSV = 39
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.356323
obj = -4.904744, rho = -0.080245
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.314338
obj = -5.545405, rho = -0.081753
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.276673
obj = -6.279994, rho = -0.056835
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.247663
obj = -7.135793, rho = -0.037224
nSV = 28, nBSV = 23
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.222214
obj = -8.063009, rho = -0.099183
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.192728
obj = -9.151219, rho = -0.068831
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.169487
obj = -10.442114, rho = -0.037750
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 188
nu = 0.151394
obj = -12.002511, rho = -0.036131
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.136598
obj = -13.754493, rho = -0.061211
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.120326
obj = -15.879397, rho = -0.039501
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.110524
obj = -18.391380, rho = 0.060057
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.099313
obj = -21.302356, rho = 0.018460
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.090120
obj = -24.736938, rho = 0.003339
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.083475
obj = -28.672944, rho = 0.096329
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.074785
obj = -33.202746, rho = 0.155563
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.067905
obj = -38.536393, rho = 0.221948
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.062494
obj = -44.680665, rho = 0.303373
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.504137
obj = -3.364042, rho = -0.183007
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.448872
obj = -3.794227, rho = -0.179611
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.403832
obj = -4.248376, rho = -0.138724
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.349155
obj = -4.755603, rho = -0.142900
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.303703
obj = -5.365556, rho = -0.137473
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.274503
obj = -6.035957, rho = -0.205327
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.235379
obj = -6.811728, rho = -0.208402
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.211103
obj = -7.721303, rho = -0.218477
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.189945
obj = -8.703319, rho = -0.229638
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.168346
obj = -9.766851, rho = -0.212534
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.147389
obj = -11.000498, rho = -0.220910
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.130485
obj = -12.333552, rho = -0.247238
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 187
nu = 0.112613
obj = -13.913127, rho = -0.232923
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.098392
obj = -15.808678, rho = -0.272270
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.086049
obj = -18.151009, rho = -0.259434
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.078255
obj = -20.935561, rho = -0.321079
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.072058
obj = -24.018825, rho = -0.398931
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.067276
obj = -27.258241, rho = -0.500482
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.061097
obj = -30.425165, rho = -0.511224
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.056433
obj = -33.326877, rho = -0.555260
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.537054
obj = -3.619956, rho = -0.183939
nSV = 56, nBSV = 49
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.478964
obj = -4.109365, rho = -0.223672
nSV = 49, nBSV = 46
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.432345
obj = -4.625458, rho = -0.200215
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.385264
obj = -5.177732, rho = -0.160247
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.348893
obj = -5.737862, rho = -0.089136
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.297439
obj = -6.329349, rho = -0.071361
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.255126
obj = -7.044973, rho = -0.050642
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.222399
obj = -7.866271, rho = -0.029442
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.200170
obj = -8.734328, rho = -0.010390
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.171354
obj = -9.670746, rho = -0.001754
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.149521
obj = -10.719764, rho = 0.015976
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.128064
obj = -11.943431, rho = -0.004550
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.111545
obj = -13.364743, rho = -0.020030
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.096209
obj = -15.033568, rho = -0.021945
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.082875
obj = -17.102378, rho = -0.023607
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.074332
obj = -19.642139, rho = -0.026559
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.068522
obj = -22.396664, rho = -0.029572
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.061475
obj = -25.330065, rho = -0.032269
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.056090
obj = -28.550523, rho = 0.074283
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.052584
obj = -31.456040, rho = 0.237616
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.521298
obj = -3.688901, rho = -0.185488
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.472219
obj = -4.256068, rho = -0.227096
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.425295
obj = -4.907377, rho = -0.209205
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.396578
obj = -5.648796, rho = -0.179281
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.357458
obj = -6.447856, rho = -0.168044
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.321040
obj = -7.338673, rho = -0.140850
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.295327
obj = -8.310539, rho = -0.163777
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.260638
obj = -9.320867, rho = -0.189823
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.226890
obj = -10.456244, rho = -0.173219
nSV = 28, nBSV = 17
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.194620
obj = -11.858310, rho = -0.163229
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.172422
obj = -13.563295, rho = -0.158843
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.155294
obj = -15.510177, rho = -0.325505
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.137568
obj = -17.790568, rho = -0.388481
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 270
nu = 0.121673
obj = -20.572994, rho = -0.411170
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.108765
obj = -24.008435, rho = -0.419921
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.102118
obj = -28.013883, rho = -0.584003
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.095873
obj = -32.301153, rho = -0.748490
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.090427
obj = -36.656284, rho = -0.724810
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.079376
obj = -41.186198, rho = -0.797477
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.069720
obj = -46.413826, rho = -0.930408
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.613915
obj = -4.094533, rho = 0.074346
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.542026
obj = -4.623316, rho = 0.061538
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.487547
obj = -5.230578, rho = 0.130449
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.434928
obj = -5.882465, rho = 0.205317
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.389126
obj = -6.557976, rho = 0.191419
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.336030
obj = -7.296644, rho = 0.186180
nSV = 37, nBSV = 27
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.290704
obj = -8.172737, rho = 0.216075
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.257610
obj = -9.181765, rho = 0.218328
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.227249
obj = -10.259865, rho = 0.202331
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.199353
obj = -11.437623, rho = 0.196307
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.179854
obj = -12.756342, rho = 0.179117
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.156481
obj = -13.994158, rho = 0.108484
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.134457
obj = -15.419317, rho = 0.139558
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.113634
obj = -17.095587, rho = 0.114528
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.098167
obj = -19.141954, rho = 0.076057
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.088184
obj = -21.400567, rho = 0.076677
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.077013
obj = -23.820676, rho = 0.117429
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.069871
obj = -26.274766, rho = 0.179768
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.065931
obj = -28.127057, rho = 0.182752
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*..*
optimization finished, #iter = 445
nu = 0.055029
obj = -28.915821, rho = 0.150728
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.544330
obj = -3.622843, rho = -0.177094
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.489603
obj = -4.082028, rho = -0.131264
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.428187
obj = -4.590100, rho = -0.172540
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.376414
obj = -5.163292, rho = -0.179673
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.326255
obj = -5.843070, rho = -0.171475
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.297071
obj = -6.612008, rho = -0.253207
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.265544
obj = -7.456844, rho = -0.312526
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.235138
obj = -8.359780, rho = -0.329415
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.209844
obj = -9.343744, rho = -0.320573
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.184311
obj = -10.384183, rho = -0.333385
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.162984
obj = -11.444809, rho = -0.247493
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.138490
obj = -12.651520, rho = -0.269926
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.119824
obj = -14.034094, rho = -0.322248
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.104931
obj = -15.606734, rho = -0.410727
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.095366
obj = -17.101039, rho = -0.579970
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.080688
obj = -18.609405, rho = -0.589235
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.069789
obj = -20.285333, rho = -0.558276
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.061918
obj = -21.804959, rho = -0.615565
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.052616
obj = -22.963358, rho = -0.623226
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.046167
obj = -23.740450, rho = -0.442200
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.495336
obj = -3.176254, rho = -0.075680
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.430670
obj = -3.539569, rho = -0.057868
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.375306
obj = -3.952643, rho = -0.074300
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.328533
obj = -4.416778, rho = -0.098247
nSV = 35, nBSV = 32
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.298035
obj = -4.887290, rho = -0.034045
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.257969
obj = -5.364693, rho = -0.025650
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.221344
obj = -5.900585, rho = 0.001442
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.193793
obj = -6.466153, rho = 0.030490
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.169965
obj = -7.000031, rho = -0.021133
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.141747
obj = -7.580096, rho = -0.007107
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.121684
obj = -8.204548, rho = 0.043859
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.104336
obj = -8.869539, rho = -0.016840
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.090277
obj = -9.461496, rho = -0.077282
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.076897
obj = -10.017557, rho = -0.048199
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.066699
obj = -10.335205, rho = -0.022011
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 298
nu = 0.052996
obj = -10.558624, rho = -0.010394
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.042944
obj = -10.797484, rho = 0.010666
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.035275
obj = -10.925444, rho = 0.048837
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.027855
obj = -10.927159, rho = 0.053957
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.021860
obj = -10.927159, rho = 0.053957
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.580000
obj = -4.027861, rho = 0.016201
nSV = 59, nBSV = 57
Total nSV = 59
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.522270
obj = -4.596950, rho = -0.027652
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.470950
obj = -5.250249, rho = 0.024993
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.426289
obj = -5.979155, rho = -0.048010
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.373005
obj = -6.819269, rho = -0.048266
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.338632
obj = -7.807185, rho = 0.016567
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.299538
obj = -8.918589, rho = 0.023125
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 23
nu = 0.278723
obj = -10.174906, rho = 0.087271
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.246409
obj = -11.441214, rho = 0.094942
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.214659
obj = -12.988398, rho = 0.116515
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.187058
obj = -14.870415, rho = 0.079556
nSV = 24, nBSV = 13
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.168400
obj = -17.189433, rho = 0.064696
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.151247
obj = -19.811816, rho = 0.079999
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.135114
obj = -22.991285, rho = 0.105362
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.122151
obj = -26.880623, rho = 0.121835
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.115791
obj = -31.237634, rho = 0.242957
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.105884
obj = -35.915265, rho = 0.404625
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.093389
obj = -41.494663, rho = 0.411531
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.084795
obj = -48.232086, rho = 0.477222
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 182
nu = 0.079335
obj = -55.828193, rho = 0.314272
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.566808
obj = -3.793859, rho = -0.143346
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.512511
obj = -4.258226, rho = -0.116079
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.452176
obj = -4.767819, rho = -0.163897
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.393637
obj = -5.322469, rho = -0.216650
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.349653
obj = -5.957328, rho = -0.193086
nSV = 37, nBSV = 33
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.308654
obj = -6.609143, rho = -0.193275
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.274322
obj = -7.322413, rho = -0.213044
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.235597
obj = -8.084595, rho = -0.217334
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.201153
obj = -8.945984, rho = -0.205202
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.177619
obj = -9.918579, rho = -0.176298
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.155648
obj = -10.938624, rho = -0.172061
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.132575
obj = -12.080160, rho = -0.180897
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.116215
obj = -13.318360, rho = -0.155822
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.100641
obj = -14.692724, rho = -0.179576
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.088376
obj = -16.140281, rho = -0.102820
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.080872
obj = -17.452212, rho = -0.124528
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 255
nu = 0.072764
obj = -18.003345, rho = 0.135384
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.058437
obj = -18.110762, rho = 0.176242
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.046287
obj = -18.163017, rho = 0.206121
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.036323
obj = -18.163014, rho = 0.206012
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.563498
obj = -3.844728, rho = -0.194042
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.501582
obj = -4.368803, rho = -0.170685
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.444577
obj = -4.988035, rho = -0.168688
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.396499
obj = -5.710151, rho = -0.174095
nSV = 44, nBSV = 37
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.359452
obj = -6.558843, rho = -0.242650
nSV = 38, nBSV = 34
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.320500
obj = -7.512483, rho = -0.253073
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.288329
obj = -8.606062, rho = -0.236093
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.257087
obj = -9.895243, rho = -0.210377
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.230135
obj = -11.410826, rho = -0.182455
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.213043
obj = -13.171996, rho = -0.252853
nSV = 23, nBSV = 19
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.192667
obj = -15.053861, rho = -0.353067
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.170328
obj = -17.298214, rho = -0.314873
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.154217
obj = -19.956636, rho = -0.293845
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.141639
obj = -22.913656, rho = -0.271963
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.125434
obj = -26.237500, rho = -0.316384
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.110672
obj = -30.320782, rho = -0.382276
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 319
nu = 0.101664
obj = -35.110723, rho = -0.385006
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.093636
obj = -40.415462, rho = -0.406392
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.*
optimization finished, #iter = 439
nu = 0.081592
obj = -46.766503, rho = -0.408051
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.072329
obj = -54.845351, rho = -0.405722
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.545731
obj = -3.581439, rho = -0.006239
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.477165
obj = -4.030506, rho = -0.001856
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.422805
obj = -4.537700, rho = -0.005944
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.371833
obj = -5.104341, rho = 0.038411
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.324410
obj = -5.776845, rho = 0.005843
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.292798
obj = -6.537087, rho = -0.023937
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.261605
obj = -7.377431, rho = -0.077308
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.233789
obj = -8.276651, rho = -0.143653
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.204633
obj = -9.264027, rho = -0.154435
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.184779
obj = -10.265377, rho = -0.095046
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.160959
obj = -11.325523, rho = -0.067034
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.138954
obj = -12.479168, rho = 0.011300
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.118556
obj = -13.813205, rho = 0.015789
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.101588
obj = -15.375939, rho = 0.018238
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 335
nu = 0.087716
obj = -17.237259, rho = 0.018968
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*..*
optimization finished, #iter = 444
nu = 0.075625
obj = -19.465217, rho = 0.021195
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*....*
optimization finished, #iter = 515
nu = 0.065236
obj = -22.269296, rho = 0.027151
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.057553
obj = -25.803424, rho = 0.039311
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.051544
obj = -30.187462, rho = 0.040823
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.046776
obj = -35.498241, rho = 0.030798
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.626418
obj = -4.317367, rho = -0.043727
nSV = 67, nBSV = 60
Total nSV = 67
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.557691
obj = -4.938916, rho = -0.033811
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.506027
obj = -5.646445, rho = -0.096520
nSV = 54, nBSV = 47
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.453753
obj = -6.432055, rho = -0.105093
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.409611
obj = -7.320200, rho = -0.106379
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.363068
obj = -8.311916, rho = -0.157328
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 96% (96/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.328599
obj = -9.451342, rho = -0.221137
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.291244
obj = -10.693117, rho = -0.267207
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.254109
obj = -12.159887, rho = -0.241963
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.227605
obj = -13.900207, rho = -0.244878
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.199634
obj = -15.944026, rho = -0.279239
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.177329
obj = -18.450701, rho = -0.323978
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.160691
obj = -21.474016, rho = -0.302327
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.144976
obj = -25.077258, rho = -0.240008
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.132802
obj = -29.377664, rho = -0.214317
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.123259
obj = -34.414806, rho = -0.308014
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.115224
obj = -40.149852, rho = -0.465735
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.106187
obj = -46.418397, rho = -0.546464
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.095133
obj = -53.678510, rho = -0.678062
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 264
nu = 0.086178
obj = -62.262813, rho = -0.748626
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.615527
obj = -4.226737, rho = -0.110133
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.555120
obj = -4.814261, rho = -0.124053
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.512557
obj = -5.450409, rho = -0.164137
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.450132
obj = -6.122162, rho = -0.162303
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.392774
obj = -6.888991, rho = -0.196126
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.345040
obj = -7.787949, rho = -0.169314
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.307482
obj = -8.830228, rho = -0.202689
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.273155
obj = -9.976426, rho = -0.139105
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.237227
obj = -11.323413, rho = -0.135752
nSV = 30, nBSV = 20
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.210468
obj = -12.926479, rho = -0.214702
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.192920
obj = -14.701527, rho = -0.208316
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.176198
obj = -16.660635, rho = -0.204891
nSV = 19, nBSV = 16
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.162684
obj = -18.459039, rho = -0.232124
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.138595
obj = -20.299062, rho = -0.213786
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.118884
obj = -22.430205, rho = -0.236568
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*..*
optimization finished, #iter = 324
nu = 0.102992
obj = -24.890087, rho = -0.334225
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.088450
obj = -27.694286, rho = -0.400583
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.078161
obj = -31.016420, rho = -0.457286
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.070372
obj = -34.195233, rho = -0.476155
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.063332
obj = -37.185005, rho = -0.335411
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.546310
obj = -3.711355, rho = -0.250979
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.497200
obj = -4.198870, rho = -0.281830
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.444962
obj = -4.714429, rho = -0.273381
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.392505
obj = -5.271538, rho = -0.304629
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.344155
obj = -5.893746, rho = -0.336781
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.298683
obj = -6.616003, rho = -0.322378
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.260671
obj = -7.434638, rho = -0.305705
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.228929
obj = -8.412228, rho = -0.329584
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.207892
obj = -9.482810, rho = -0.336919
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 195
nu = 0.180310
obj = -10.656233, rho = -0.332690
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.157472
obj = -12.059015, rho = -0.348118
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.142476
obj = -13.681358, rho = -0.275061
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.124948
obj = -15.446731, rho = -0.249206
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.109022
obj = -17.574656, rho = -0.246235
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.096879
obj = -20.162603, rho = -0.265268
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.088929
obj = -22.989381, rho = -0.248948
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.081653
obj = -26.051491, rho = -0.341326
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.073487
obj = -29.137444, rho = -0.488957
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.066303
obj = -32.266858, rho = -0.672237
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 310
nu = 0.059128
obj = -35.081936, rho = -0.759483
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.581328
obj = -3.948038, rho = 0.122581
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.525683
obj = -4.475486, rho = 0.082764
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.468783
obj = -5.058799, rho = 0.117588
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.411809
obj = -5.720154, rho = 0.110794
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.371798
obj = -6.464651, rho = 0.174381
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.325484
obj = -7.265818, rho = 0.216521
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.287251
obj = -8.203787, rho = 0.210787
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.256974
obj = -9.262041, rho = 0.260394
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.225404
obj = -10.429899, rho = 0.297961
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.198496
obj = -11.794528, rho = 0.249208
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.178481
obj = -13.286872, rho = 0.229690
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.162157
obj = -14.861286, rho = 0.394434
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.148695
obj = -16.295586, rho = 0.515121
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.127495
obj = -17.529287, rho = 0.558059
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.110013
obj = -18.744787, rho = 0.647333
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.093796
obj = -19.809142, rho = 0.694823
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.082551
obj = -20.413528, rho = 0.746826
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*
optimization finished, #iter = 666
nu = 0.066313
obj = -20.493011, rho = 0.735334
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.......*.*
optimization finished, #iter = 809
nu = 0.052225
obj = -20.494595, rho = 0.736664
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.......*.*
optimization finished, #iter = 809
nu = 0.040984
obj = -20.494595, rho = 0.736664
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.517748
obj = -3.540071, rho = -0.146332
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.462304
obj = -4.034158, rho = -0.116044
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.420000
obj = -4.599806, rho = -0.102560
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.380100
obj = -5.190793, rho = -0.138705
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.338381
obj = -5.819363, rho = -0.192409
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.298615
obj = -6.498096, rho = -0.160206
nSV = 35, nBSV = 26
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*...*
optimization finished, #iter = 463
nu = 0.261871
obj = -7.246622, rho = -0.171058
nSV = 32, nBSV = 21
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.224968
obj = -8.141778, rho = -0.165407
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.201702
obj = -9.118576, rho = -0.131870
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.181233
obj = -10.160888, rho = -0.138872
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.163702
obj = -11.110550, rho = -0.120756
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.138820
obj = -12.098342, rho = -0.103086
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.119586
obj = -13.175826, rho = -0.088073
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 241
nu = 0.100959
obj = -14.312866, rho = -0.043646
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 283
nu = 0.085682
obj = -15.651316, rho = -0.029222
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.072069
obj = -17.228185, rho = -0.054355
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.062598
obj = -19.061371, rho = -0.160257
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.056192
obj = -20.958210, rho = -0.254134
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.050411
obj = -22.444011, rho = -0.300833
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*
optimization finished, #iter = 234
nu = 0.043255
obj = -23.600033, rho = -0.182264
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.573566
obj = -3.864749, rho = -0.228686
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.516168
obj = -4.362336, rho = -0.199572
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.457929
obj = -4.921582, rho = -0.215995
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.401162
obj = -5.547182, rho = -0.214053
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.356507
obj = -6.261646, rho = -0.168098
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.321613
obj = -7.020660, rho = -0.159629
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.276918
obj = -7.882889, rho = -0.156151
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.242321
obj = -8.913983, rho = -0.194159
nSV = 27, nBSV = 23
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.215989
obj = -10.091587, rho = -0.237091
nSV = 24, nBSV = 20
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.196889
obj = -11.345410, rho = -0.286874
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.174701
obj = -12.655976, rho = -0.354644
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.151060
obj = -14.101394, rho = -0.355663
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.132377
obj = -15.797214, rho = -0.338459
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.120687
obj = -17.554227, rho = -0.308363
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.104129
obj = -19.307683, rho = -0.356789
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.089061
obj = -21.313320, rho = -0.387099
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.077202
obj = -23.627417, rho = -0.493849
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.068081
obj = -26.157202, rho = -0.497346
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
...*.*
optimization finished, #iter = 424
nu = 0.058788
obj = -28.863603, rho = -0.459471
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
...*.*
optimization finished, #iter = 498
nu = 0.050616
obj = -31.837278, rho = -0.461315
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.569435
obj = -3.968381, rho = -0.044817
nSV = 58, nBSV = 55
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.514889
obj = -4.537863, rho = -0.039855
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.467352
obj = -5.182433, rho = -0.025612
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.417237
obj = -5.901317, rho = -0.019220
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.368803
obj = -6.738048, rho = 0.003109
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.326733
obj = -7.723022, rho = 0.020830
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.290179
obj = -8.923189, rho = 0.006563
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.263780
obj = -10.323466, rho = 0.111560
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.235614
obj = -12.007315, rho = 0.065131
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.214040
obj = -13.998972, rho = 0.030597
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.190917
obj = -16.466206, rho = 0.037054
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.178272
obj = -19.448258, rho = 0.128110
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.168566
obj = -22.865274, rho = 0.173465
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.156575
obj = -26.657809, rho = 0.184314
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.140963
obj = -31.134924, rho = 0.142000
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.125610
obj = -36.672695, rho = 0.141963
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.115615
obj = -43.630248, rho = 0.158264
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.106340
obj = -52.005058, rho = 0.201270
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.101140
obj = -62.057714, rho = 0.364441
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 97% (97/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.093602
obj = -74.004992, rho = 0.303234
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.602195
obj = -4.020593, rho = -0.038767
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.526861
obj = -4.558238, rho = -0.061747
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.468257
obj = -5.176765, rho = -0.051612
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.418797
obj = -5.897611, rho = -0.063288
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.384712
obj = -6.680182, rho = -0.187654
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.341804
obj = -7.496471, rho = -0.302838
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.296527
obj = -8.425847, rho = -0.292383
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.265069
obj = -9.478146, rho = -0.281129
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.231598
obj = -10.626646, rho = -0.332877
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.199407
obj = -12.014059, rho = -0.359691
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.176775
obj = -13.720850, rho = -0.358687
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.157271
obj = -15.693646, rho = -0.370417
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.142124
obj = -17.952753, rho = -0.455776
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.128589
obj = -20.451290, rho = -0.468485
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.113615
obj = -23.297477, rho = -0.468591
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.104168
obj = -26.547244, rho = -0.540564
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 261
nu = 0.093688
obj = -30.025967, rho = -0.703444
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.083298
obj = -33.627522, rho = -0.865131
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 258
nu = 0.073329
obj = -37.841059, rho = -0.959936
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*....*
optimization finished, #iter = 674
nu = 0.064852
obj = -42.368718, rho = -1.027958
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.566083
obj = -3.834212, rho = -0.035369
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 96% (96/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.503767
obj = -4.346903, rho = -0.058145
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 96% (96/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.441609
obj = -4.953084, rho = -0.083965
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.397483
obj = -5.664587, rho = -0.108291
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.360229
obj = -6.457510, rho = -0.131659
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.324120
obj = -7.335062, rho = -0.151300
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.289220
obj = -8.320506, rho = -0.217042
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.259177
obj = -9.416904, rho = -0.259130
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.230376
obj = -10.628881, rho = -0.353131
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.200917
obj = -12.021816, rho = -0.387960
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.175819
obj = -13.707553, rho = -0.409134
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.158470
obj = -15.635326, rho = -0.446128
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.138644
obj = -17.889276, rho = -0.445048
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.124305
obj = -20.672284, rho = -0.538671
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.114241
obj = -23.798013, rho = -0.679672
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.105529
obj = -27.293878, rho = -0.700600
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*...*
optimization finished, #iter = 303
nu = 0.100681
obj = -30.626148, rho = -0.730467
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 283
nu = 0.086363
obj = -33.754687, rho = -0.721026
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.072843
obj = -37.681592, rho = -0.720697
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.065474
obj = -42.230893, rho = -0.949150
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.567145
obj = -3.959562, rho = -0.197230
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.519041
obj = -4.529565, rho = -0.191465
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.469146
obj = -5.136794, rho = -0.178738
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.420206
obj = -5.817202, rho = -0.183921
nSV = 45, nBSV = 39
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.371067
obj = -6.563489, rho = -0.141635
nSV = 39, nBSV = 34
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.323952
obj = -7.454470, rho = -0.149525
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.284981
obj = -8.514600, rho = -0.163270
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.260168
obj = -9.738787, rho = -0.109962
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.231819
obj = -11.147944, rho = -0.070553
nSV = 26, nBSV = 22
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.211229
obj = -12.729941, rho = -0.086982
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.191071
obj = -14.419763, rho = -0.173979
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.173225
obj = -16.245593, rho = -0.397535
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.152345
obj = -18.132988, rho = -0.477340
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.133230
obj = -20.219675, rho = -0.474522
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.116547
obj = -22.642842, rho = -0.483778
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.103274
obj = -25.298406, rho = -0.545159
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.089339
obj = -28.336481, rho = -0.630910
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.081390
obj = -31.676455, rho = -0.913567
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.074824
obj = -34.529803, rho = -0.997027
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 296
nu = 0.064955
obj = -36.698630, rho = -1.016744
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.538805
obj = -3.694903, rho = -0.138162
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.486035
obj = -4.203968, rho = -0.100624
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.432198
obj = -4.772162, rho = -0.161642
nSV = 48, nBSV = 41
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.387262
obj = -5.420141, rho = -0.207928
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.344572
obj = -6.166350, rho = -0.203603
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.308191
obj = -6.996276, rho = -0.202125
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.268979
obj = -7.955890, rho = -0.226436
nSV = 32, nBSV = 22
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.239283
obj = -9.136966, rho = -0.208461
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.218169
obj = -10.467546, rho = -0.118304
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.192931
obj = -11.997582, rho = -0.076944
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.180784
obj = -13.686767, rho = -0.238831
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.163216
obj = -15.448354, rho = -0.266669
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.146977
obj = -17.283938, rho = -0.311099
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.131224
obj = -19.051729, rho = -0.331989
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 306
nu = 0.112287
obj = -20.967426, rho = -0.234310
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.100485
obj = -23.049335, rho = -0.184895
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.091338
obj = -24.672141, rho = 0.009356
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.075251
obj = -25.857033, rho = 0.060876
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.060959
obj = -27.327573, rho = 0.038323
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.049868
obj = -29.192100, rho = 0.008188
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.627940
obj = -4.347471, rho = -0.161080
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.564164
obj = -4.977765, rho = -0.082392
nSV = 59, nBSV = 55
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.509814
obj = -5.688979, rho = -0.047460
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.464497
obj = -6.491075, rho = -0.047913
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.409948
obj = -7.348614, rho = -0.019722
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.359420
obj = -8.371300, rho = -0.015053
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.317892
obj = -9.623571, rho = -0.006690
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.284349
obj = -11.111639, rho = 0.023705
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.255324
obj = -12.888805, rho = 0.024910
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.232052
obj = -15.012044, rho = 0.110385
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.215046
obj = -17.478071, rho = 0.138119
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.199602
obj = -20.209539, rho = 0.179581
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.181267
obj = -23.185358, rho = 0.145754
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.**..*
optimization finished, #iter = 278
nu = 0.162387
obj = -26.602038, rho = 0.091233
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.151634
obj = -30.354884, rho = 0.139937
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*...*
optimization finished, #iter = 455
nu = 0.135737
obj = -34.193922, rho = 0.100669
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.118050
obj = -38.766231, rho = 0.066458
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.106996
obj = -44.020835, rho = -0.046179
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.098272
obj = -49.415952, rho = -0.280879
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.087395
obj = -54.638594, rho = -0.239810
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.535411
obj = -3.587693, rho = 0.041154
nSV = 56, nBSV = 50
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.480000
obj = -4.055425, rho = 0.080161
nSV = 50, nBSV = 46
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.426672
obj = -4.563468, rho = 0.073980
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.372053
obj = -5.138464, rho = 0.085003
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.330967
obj = -5.799095, rho = 0.062407
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.299908
obj = -6.506342, rho = 0.028487
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.263104
obj = -7.262543, rho = 0.021686
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.237340
obj = -8.050131, rho = -0.006282
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.205590
obj = -8.812901, rho = -0.023744
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.178921
obj = -9.668172, rho = -0.062353
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.158623
obj = -10.461267, rho = -0.114396
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.135689
obj = -11.159047, rho = -0.140493
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.116288
obj = -11.814130, rho = -0.171043
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*..*
optimization finished, #iter = 444
nu = 0.095413
obj = -12.389757, rho = -0.168979
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.078545
obj = -12.975003, rho = -0.165611
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.066652
obj = -13.529972, rho = -0.121437
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.054491
obj = -13.902023, rho = -0.069005
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 265
nu = 0.044265
obj = -14.230354, rho = -0.042356
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.036109
obj = -14.477320, rho = -0.017603
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.029022
obj = -14.510169, rho = -0.004105
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.569636
obj = -3.980420, rho = -0.198231
nSV = 61, nBSV = 54
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.515137
obj = -4.564431, rho = -0.216237
nSV = 55, nBSV = 47
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.458027
obj = -5.247161, rho = -0.260616
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.414891
obj = -6.037199, rho = -0.211783
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.374452
obj = -6.921120, rho = -0.189582
nSV = 42, nBSV = 35
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.338069
obj = -7.953861, rho = -0.283273
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.307874
obj = -9.131796, rho = -0.264692
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.278275
obj = -10.412926, rho = -0.170949
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.246946
obj = -11.909237, rho = -0.178916
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.229274
obj = -13.509937, rho = -0.049857
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.200156
obj = -15.295020, rho = -0.074954
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.178652
obj = -17.344552, rho = -0.074197
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.161239
obj = -19.632460, rho = -0.055325
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.142211
obj = -22.107489, rho = -0.053521
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.127370
obj = -24.974666, rho = 0.082963
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.115311
obj = -27.978061, rho = 0.071596
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*..*
optimization finished, #iter = 355
nu = 0.104086
obj = -30.701684, rho = -0.092376
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*..*
optimization finished, #iter = 418
nu = 0.088161
obj = -33.618409, rho = -0.149503
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.075088
obj = -37.023548, rho = -0.220339
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.068115
obj = -40.550812, rho = -0.576784
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.584450
obj = -3.945168, rho = -0.102143
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.516541
obj = -4.483526, rho = -0.107362
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.459768
obj = -5.117953, rho = -0.100313
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.421144
obj = -5.809541, rho = -0.117100
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.376711
obj = -6.534596, rho = -0.110835
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.333539
obj = -7.358040, rho = -0.164522
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.297302
obj = -8.206137, rho = -0.247133
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.262723
obj = -9.115065, rho = -0.233236
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.229410
obj = -10.119641, rho = -0.297701
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.198296
obj = -11.185464, rho = -0.297653
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.170779
obj = -12.443338, rho = -0.373560
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.146405
obj = -13.946748, rho = -0.365039
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.136186
obj = -15.562041, rho = -0.209655
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.118811
obj = -17.000955, rho = -0.148948
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.101700
obj = -18.622055, rho = -0.171392
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.085524
obj = -20.453610, rho = -0.219277
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.073925
obj = -22.668296, rho = -0.261265
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.066972
obj = -24.928008, rho = -0.364366
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.059858
obj = -26.699843, rho = -0.465138
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.051179
obj = -28.081956, rho = -0.439802
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.530781
obj = -3.517628, rho = -0.030042
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.475080
obj = -3.964319, rho = -0.066812
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.414596
obj = -4.445726, rho = -0.068167
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.374179
obj = -4.995439, rho = -0.050090
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.338641
obj = -5.514151, rho = -0.024511
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.291339
obj = -6.043687, rho = 0.001659
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.255321
obj = -6.609678, rho = -0.003370
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.215570
obj = -7.191993, rho = -0.030000
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.184338
obj = -7.869416, rho = -0.067442
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.157330
obj = -8.630045, rho = -0.062645
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.138947
obj = -9.389547, rho = -0.067167
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.119560
obj = -10.151312, rho = -0.003782
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.101556
obj = -10.906372, rho = 0.024991
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.087553
obj = -11.676176, rho = 0.051656
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.072455
obj = -12.396454, rho = 0.078643
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.065233
obj = -12.966725, rho = -0.086347
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.052635
obj = -13.217299, rho = -0.077494
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 293
nu = 0.043262
obj = -13.321101, rho = -0.194026
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 293
nu = 0.033950
obj = -13.321101, rho = -0.194026
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 293
nu = 0.026643
obj = -13.321101, rho = -0.194026
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.527729
obj = -3.466182, rho = -0.069692
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.471519
obj = -3.882584, rho = -0.116229
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.412531
obj = -4.329907, rho = -0.122722
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.362261
obj = -4.832793, rho = -0.115691
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.320004
obj = -5.369950, rho = -0.187626
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.280274
obj = -5.965263, rho = -0.225033
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.246832
obj = -6.587833, rho = -0.173602
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.215793
obj = -7.205295, rho = -0.184526
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.186262
obj = -7.885865, rho = -0.170397
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.160495
obj = -8.559695, rho = -0.145720
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.137393
obj = -9.304105, rho = -0.119763
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.119132
obj = -10.021335, rho = -0.104138
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.098566
obj = -10.777071, rho = -0.115475
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.082171
obj = -11.693737, rho = -0.105601
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.070044
obj = -12.786718, rho = -0.004890
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.062532
obj = -13.891196, rho = 0.004977
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.055341
obj = -14.630750, rho = -0.002057
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.048672
obj = -14.988427, rho = 0.009545
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.038196
obj = -14.988427, rho = 0.009545
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.029974
obj = -14.988427, rho = 0.009545
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.565542
obj = -3.833628, rho = -0.181624
nSV = 61, nBSV = 53
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.505145
obj = -4.342317, rho = -0.157234
nSV = 55, nBSV = 46
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.450848
obj = -4.924783, rho = -0.138593
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.401392
obj = -5.578407, rho = -0.153160
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.360000
obj = -6.322601, rho = -0.124473
nSV = 37, nBSV = 34
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.318069
obj = -7.117788, rho = -0.086025
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.284691
obj = -8.027892, rho = -0.088661
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.255051
obj = -9.017376, rho = -0.145106
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.220398
obj = -10.104004, rho = -0.157653
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 248
nu = 0.192165
obj = -11.406954, rho = -0.141251
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.169479
obj = -12.919412, rho = -0.131392
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.157229
obj = -14.495090, rho = -0.109410
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.135159
obj = -16.160290, rho = -0.147929
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.115912
obj = -18.199496, rho = -0.146293
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*...*
optimization finished, #iter = 372
nu = 0.101813
obj = -20.670125, rho = -0.152313
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.090628
obj = -23.556861, rho = -0.092985
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 264
nu = 0.080944
obj = -26.847302, rho = -0.021713
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.074516
obj = -30.449861, rho = -0.047266
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.066513
obj = -34.096812, rho = -0.083953
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.059119
obj = -37.925268, rho = -0.128406
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.582314
obj = -3.978297, rho = -0.081645
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.520288
obj = -4.545408, rho = -0.049949
nSV = 54, nBSV = 51
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.469623
obj = -5.148534, rho = -0.010382
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.412589
obj = -5.853820, rho = -0.017250
nSV = 47, nBSV = 39
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.367611
obj = -6.668318, rho = 0.002854
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.325296
obj = -7.627774, rho = -0.010670
nSV = 37, nBSV = 27
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.290212
obj = -8.775179, rho = -0.030325
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.264362
obj = -10.104401, rho = 0.009379
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.246691
obj = -11.570312, rho = 0.074015
nSV = 26, nBSV = 23
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.218537
obj = -13.085086, rho = 0.110576
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.193884
obj = -14.831468, rho = 0.157852
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.171694
obj = -16.878156, rho = 0.145189
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.149012
obj = -19.331784, rho = 0.142726
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.130874
obj = -22.399532, rho = 0.142740
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.116741
obj = -26.243912, rho = 0.124830
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.106378
obj = -31.018434, rho = 0.130642
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 97% (97/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.102066
obj = -36.569005, rho = 0.199547
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 97% (97/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.095534
obj = -42.609007, rho = 0.249097
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.087313
obj = -49.498642, rho = 0.246785
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 393
nu = 0.079533
obj = -57.398937, rho = 0.223263
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.544417
obj = -3.581708, rho = -0.123756
nSV = 56, nBSV = 53
Total nSV = 56
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.480868
obj = -4.009028, rho = -0.123727
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.424186
obj = -4.486026, rho = -0.093594
nSV = 44, nBSV = 41
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.373630
obj = -5.010080, rho = -0.087330
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.330213
obj = -5.572844, rho = -0.084068
nSV = 38, nBSV = 30
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.290126
obj = -6.205986, rho = -0.127770
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*..*
optimization finished, #iter = 221
nu = 0.255881
obj = -6.848442, rho = -0.200548
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.220159
obj = -7.568479, rho = -0.206342
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.194000
obj = -8.335779, rho = -0.262174
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.164438
obj = -9.166754, rho = -0.265421
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.143554
obj = -10.113444, rho = -0.239291
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.126933
obj = -11.022320, rho = -0.355591
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.113076
obj = -11.930102, rho = -0.281367
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.097823
obj = -12.537630, rho = -0.373416
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.082669
obj = -13.027192, rho = -0.409096
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.068101
obj = -13.297441, rho = -0.411273
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 235
nu = 0.054330
obj = -13.479454, rho = -0.417343
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 255
nu = 0.044080
obj = -13.574680, rho = -0.437246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 255
nu = 0.034592
obj = -13.574680, rho = -0.437246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 255
nu = 0.027147
obj = -13.574680, rho = -0.437246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.609829
obj = -4.294034, rho = -0.030697
nSV = 63, nBSV = 59
Total nSV = 63
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.560000
obj = -4.927149, rho = 0.008977
nSV = 57, nBSV = 55
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.511368
obj = -5.604497, rho = 0.021625
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.465417
obj = -6.342152, rho = -0.009944
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.414182
obj = -7.123320, rho = -0.022071
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.370182
obj = -7.936352, rho = 0.041166
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.319956
obj = -8.833342, rho = 0.048896
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.274558
obj = -9.905109, rho = 0.027848
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.240064
obj = -11.157938, rho = 0.064112
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.211761
obj = -12.629329, rho = 0.118099
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.190900
obj = -14.233667, rho = 0.081531
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.172561
obj = -15.926581, rho = -0.014490
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.149893
obj = -17.699382, rho = -0.066734
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.128194
obj = -19.838774, rho = -0.059365
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.111820
obj = -22.412678, rho = -0.077666
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.098840
obj = -25.479603, rho = -0.047111
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.089163
obj = -28.854339, rho = 0.042035
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 279
nu = 0.079989
obj = -32.592390, rho = 0.165605
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.069929
obj = -36.797698, rho = 0.235809
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.062482
obj = -41.587946, rho = 0.334720
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.584559
obj = -3.962280, rho = -0.187156
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.527173
obj = -4.478483, rho = -0.171692
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.463027
obj = -5.059689, rho = -0.174975
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.409832
obj = -5.731466, rho = -0.226579
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.362552
obj = -6.514134, rho = -0.272315
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.333174
obj = -7.390855, rho = -0.201704
nSV = 35, nBSV = 31
Total nSV = 35
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.301583
obj = -8.263517, rho = -0.191760
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.262428
obj = -9.213987, rho = -0.207587
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.229529
obj = -10.254214, rho = -0.244090
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.202728
obj = -11.420361, rho = -0.321003
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.181210
obj = -12.533011, rho = -0.423709
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.154514
obj = -13.742720, rho = -0.310224
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.130777
obj = -15.119366, rho = -0.299074
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.114009
obj = -16.771297, rho = -0.200107
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.099389
obj = -18.463043, rho = -0.141361
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 198
nu = 0.089006
obj = -20.100563, rho = -0.225706
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 357
nu = 0.077977
obj = -21.545622, rho = -0.369722
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*......*
optimization finished, #iter = 776
nu = 0.065052
obj = -22.965089, rho = -0.438045
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 422
nu = 0.054600
obj = -24.434298, rho = -0.504842
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.044753
obj = -26.052654, rho = -0.494243
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.627300
obj = -4.337410, rho = -0.202684
nSV = 65, nBSV = 59
Total nSV = 65
Accuracy = 95% (95/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.560520
obj = -4.973701, rho = -0.190798
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.504462
obj = -5.692372, rho = -0.200661
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 95% (95/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.451813
obj = -6.506880, rho = -0.173055
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.404018
obj = -7.457195, rho = -0.223864
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.357924
obj = -8.583499, rho = -0.237088
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.329793
obj = -9.916231, rho = -0.178148
nSV = 37, nBSV = 28
Total nSV = 37
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.292113
obj = -11.452221, rho = -0.161783
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.260735
obj = -13.323229, rho = -0.162815
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.237111
obj = -15.604538, rho = -0.184536
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 173
nu = 0.213260
obj = -18.341593, rho = -0.181593
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.193558
obj = -21.766510, rho = -0.145554
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.182253
obj = -25.908411, rho = -0.070757
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.170146
obj = -30.747217, rho = 0.047141
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.158272
obj = -36.569856, rho = 0.149042
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.146375
obj = -43.505444, rho = 0.210253
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.138744
obj = -51.757829, rho = 0.331669
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.128226
obj = -61.392566, rho = 0.402353
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.116191
obj = -73.382629, rho = 0.461470
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*.*
optimization finished, #iter = 440
nu = 0.106590
obj = -88.589043, rho = 0.503708
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.545148
obj = -3.710507, rho = -0.174023
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.483065
obj = -4.218753, rho = -0.161207
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.433765
obj = -4.803051, rho = -0.122683
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.390340
obj = -5.459346, rho = -0.069466
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.343371
obj = -6.194965, rho = -0.049895
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.303602
obj = -7.081155, rho = -0.055405
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.284301
obj = -8.033756, rho = 0.004998
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.253951
obj = -9.013506, rho = -0.079838
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.230698
obj = -10.002387, rho = -0.039869
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.197820
obj = -11.012429, rho = -0.007044
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.171750
obj = -12.155384, rho = -0.028261
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.159089
obj = -13.285276, rho = -0.202975
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.133230
obj = -14.208255, rho = -0.239623
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.109891
obj = -15.293684, rho = -0.241635
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.093631
obj = -16.528354, rho = -0.203106
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.082600
obj = -17.681027, rho = -0.119286
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.067789
obj = -18.720086, rho = -0.119973
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.058165
obj = -19.710866, rho = -0.071455
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.048051
obj = -20.528982, rho = -0.095513
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.039112
obj = -21.377180, rho = -0.087461
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.581866
obj = -3.878044, rho = -0.187373
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.521791
obj = -4.364053, rho = -0.274494
nSV = 55, nBSV = 48
Total nSV = 55
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.455632
obj = -4.908988, rho = -0.300537
nSV = 49, nBSV = 42
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.397984
obj = -5.542669, rho = -0.307384
nSV = 45, nBSV = 35
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.349097
obj = -6.311713, rho = -0.325598
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 96% (96/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.315488
obj = -7.176534, rho = -0.345493
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.279866
obj = -8.151968, rho = -0.355852
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.250247
obj = -9.272780, rho = -0.364264
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.220839
obj = -10.595021, rho = -0.357664
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.202278
obj = -11.971097, rho = -0.291549
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.178478
obj = -13.579374, rho = -0.310112
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.158638
obj = -15.346866, rho = -0.302136
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.137769
obj = -17.477702, rho = -0.309275
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.123987
obj = -20.017864, rho = -0.345707
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.113234
obj = -22.782863, rho = -0.238345
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.103235
obj = -25.685586, rho = -0.196465
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*..*
optimization finished, #iter = 301
nu = 0.088902
obj = -28.952108, rho = -0.237010
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.076857
obj = -33.012425, rho = -0.282868
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.069802
obj = -37.967883, rho = -0.322758
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.066648
obj = -42.945265, rho = -0.357476
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.598246
obj = -4.119443, rho = -0.091403
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.541314
obj = -4.690256, rho = -0.038038
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.490095
obj = -5.317893, rho = 0.022662
nSV = 52, nBSV = 45
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.427484
obj = -6.027231, rho = 0.022170
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.380175
obj = -6.866901, rho = -0.000909
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.339538
obj = -7.836993, rho = -0.015727
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.302561
obj = -8.940258, rho = 0.000875
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.272242
obj = -10.221373, rho = 0.012757
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.244473
obj = -11.645305, rho = 0.024627
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.218303
obj = -13.276406, rho = 0.058887
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.192344
obj = -15.193427, rho = 0.057970
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.171809
obj = -17.499116, rho = 0.048445
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.157371
obj = -20.143261, rho = -0.025945
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.146763
obj = -22.991746, rho = -0.107127
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.138989
obj = -25.665982, rho = -0.124989
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
....*....*
optimization finished, #iter = 804
nu = 0.120328
obj = -28.015702, rho = -0.105010
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*..*
optimization finished, #iter = 409
nu = 0.103516
obj = -30.663828, rho = -0.109945
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.089750
obj = -33.339348, rho = -0.095653
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.......*..........*
optimization finished, #iter = 1785
nu = 0.075623
obj = -36.317806, rho = -0.095183
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*....*
optimization finished, #iter = 649
nu = 0.066233
obj = -39.476870, rho = -0.146733
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.538306
obj = -3.636512, rho = -0.244944
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.482419
obj = -4.114539, rho = -0.336104
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.430496
obj = -4.638787, rho = -0.335014
nSV = 46, nBSV = 40
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.378387
obj = -5.228727, rho = -0.333229
nSV = 42, nBSV = 33
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.333349
obj = -5.931861, rho = -0.304091
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.296133
obj = -6.739833, rho = -0.341420
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.259254
obj = -7.684758, rho = -0.385165
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.233968
obj = -8.786615, rho = -0.340158
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.206886
obj = -10.034670, rho = -0.328258
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.190744
obj = -11.429756, rho = -0.518843
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.170310
obj = -12.946680, rho = -0.609442
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.152761
obj = -14.647838, rho = -0.574247
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.134619
obj = -16.513021, rho = -0.582611
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.124936
obj = -18.505249, rho = -0.424829
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.111334
obj = -20.348101, rho = -0.531719
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.094254
obj = -22.315593, rho = -0.571175
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.080956
obj = -24.635596, rho = -0.618127
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.073310
obj = -27.099837, rho = -0.646249
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.067008
obj = -28.708663, rho = -0.560284
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.058445
obj = -29.423182, rho = -0.472481
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.529864
obj = -3.624527, rho = 0.063449
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.473469
obj = -4.122321, rho = 0.002257
nSV = 52, nBSV = 44
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.420001
obj = -4.708969, rho = -0.024413
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.382175
obj = -5.365570, rho = 0.040075
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.340618
obj = -6.067842, rho = -0.000073
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.301394
obj = -6.876308, rho = 0.022880
nSV = 34, nBSV = 26
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.264048
obj = -7.846204, rho = 0.048586
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.241145
obj = -8.939261, rho = 0.019295
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.211208
obj = -10.200333, rho = 0.035058
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.187453
obj = -11.709187, rho = 0.102677
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.172693
obj = -13.453049, rho = 0.199996
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.158292
obj = -15.349762, rho = 0.147328
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.142827
obj = -17.341971, rho = -0.052747
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.129737
obj = -19.434387, rho = -0.106866
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.116785
obj = -21.412637, rho = -0.093838
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 265
nu = 0.101315
obj = -23.413550, rho = -0.066397
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 299
nu = 0.087092
obj = -25.602251, rho = -0.122643
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.073226
obj = -28.052103, rho = -0.141577
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.063093
obj = -30.864281, rho = -0.177116
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.055968
obj = -33.816331, rho = -0.206456
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.590374
obj = -4.083529, rho = -0.226454
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.525623
obj = -4.675181, rho = -0.239097
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.472355
obj = -5.354473, rho = -0.264082
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.422399
obj = -6.150738, rho = -0.249480
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.373195
obj = -7.099123, rho = -0.265267
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.336117
obj = -8.244041, rho = -0.222763
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.316766
obj = -9.523947, rho = -0.229518
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.281140
obj = -10.981753, rho = -0.245371
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.262053
obj = -12.699839, rho = -0.292436
nSV = 29, nBSV = 25
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.244820
obj = -14.464265, rho = -0.347035
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.230512
obj = -16.106391, rho = -0.395609
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.200704
obj = -17.605045, rho = -0.396217
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 261
nu = 0.173630
obj = -19.097661, rho = -0.431014
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.145391
obj = -20.779873, rho = -0.430585
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.124399
obj = -22.729046, rho = -0.484071
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.108908
obj = -24.798053, rho = -0.618913
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 177
nu = 0.094702
obj = -26.826424, rho = -0.707511
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*....*
optimization finished, #iter = 476
nu = 0.082975
obj = -28.494745, rho = -0.869139
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.067973
obj = -30.028902, rho = -0.936923
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.056611
obj = -31.745131, rho = -1.030679
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.593704
obj = -3.963154, rho = -0.263130
nSV = 62, nBSV = 57
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.529414
obj = -4.474798, rho = -0.286333
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.465630
obj = -5.037232, rho = -0.307752
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.412908
obj = -5.690685, rho = -0.375798
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.374710
obj = -6.392222, rho = -0.416245
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.326144
obj = -7.134875, rho = -0.425241
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.284944
obj = -7.992537, rho = -0.476537
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.247179
obj = -8.979633, rho = -0.493909
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.216043
obj = -10.172626, rho = -0.516535
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.195963
obj = -11.527713, rho = -0.616273
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.177428
obj = -12.926776, rho = -0.531795
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.155318
obj = -14.434021, rho = -0.573563
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.140841
obj = -15.938584, rho = -0.679404
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 280
nu = 0.119281
obj = -17.532927, rho = -0.639735
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.101080
obj = -19.474871, rho = -0.583207
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.086718
obj = -21.850894, rho = -0.516048
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 335
nu = 0.075615
obj = -24.735314, rho = -0.487048
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.066486
obj = -28.163062, rho = -0.413463
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.....*.*
optimization finished, #iter = 779
nu = 0.059039
obj = -32.234087, rho = -0.372649
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.054902
obj = -36.815670, rho = -0.224697
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.609864
obj = -4.318800, rho = -0.083745
nSV = 62, nBSV = 60
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.562411
obj = -4.942008, rho = -0.022921
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.507665
obj = -5.642323, rho = 0.028410
nSV = 53, nBSV = 48
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.468729
obj = -6.354662, rho = -0.072094
nSV = 51, nBSV = 43
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.411632
obj = -7.118279, rho = -0.095568
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.362140
obj = -7.968371, rho = -0.014803
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.324946
obj = -8.910688, rho = -0.044193
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.287144
obj = -9.858997, rho = -0.074016
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.251544
obj = -10.855187, rho = -0.086837
nSV = 31, nBSV = 21
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.....*
optimization finished, #iter = 506
nu = 0.215857
obj = -11.926725, rho = -0.142114
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.189304
obj = -13.133562, rho = -0.206810
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.164009
obj = -14.283321, rho = -0.251807
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.140531
obj = -15.519963, rho = -0.344755
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.117020
obj = -16.940450, rho = -0.322015
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.102347
obj = -18.535626, rho = -0.256775
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.089176
obj = -20.052341, rho = -0.219090
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.079206
obj = -21.413093, rho = -0.445403
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.066614
obj = -22.414527, rho = -0.542446
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 180
nu = 0.055204
obj = -23.312581, rho = -0.465554
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.047261
obj = -23.633053, rho = -0.311977
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.553202
obj = -3.596412, rho = -0.124746
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.486581
obj = -4.025949, rho = -0.137334
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.427936
obj = -4.495036, rho = -0.170097
nSV = 46, nBSV = 38
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.375395
obj = -5.025257, rho = -0.097871
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.323849
obj = -5.630729, rho = -0.097610
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.286678
obj = -6.315907, rho = -0.112037
nSV = 32, nBSV = 26
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.253257
obj = -7.085551, rho = -0.182883
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.222056
obj = -7.957922, rho = -0.238279
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.195764
obj = -8.917275, rho = -0.216441
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.174238
obj = -9.987413, rho = -0.228621
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 285
nu = 0.153357
obj = -11.161791, rho = -0.281641
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.134328
obj = -12.402089, rho = -0.328788
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.119410
obj = -13.773017, rho = -0.418312
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 199
nu = 0.105154
obj = -15.104967, rho = -0.538399
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.090056
obj = -16.575823, rho = -0.663903
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.076574
obj = -18.186136, rho = -0.742831
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.068848
obj = -19.952363, rho = -0.840278
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.060328
obj = -21.320423, rho = -0.911791
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.050491
obj = -22.663097, rho = -0.913766
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.043253
obj = -23.943193, rho = -0.854197
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.530522
obj = -3.656881, rho = -0.093764
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.471041
obj = -4.181033, rho = -0.087791
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.422490
obj = -4.796541, rho = -0.057771
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 96% (96/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.378998
obj = -5.514857, rho = -0.121239
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.343259
obj = -6.338661, rho = -0.176999
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 96% (96/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.312547
obj = -7.278643, rho = -0.214669
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.281340
obj = -8.308238, rho = -0.213498
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.258326
obj = -9.451077, rho = -0.220279
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.232487
obj = -10.670308, rho = -0.281925
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.204835
obj = -12.016006, rho = -0.221104
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.178541
obj = -13.569690, rho = -0.230867
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.155658
obj = -15.456639, rho = -0.242205
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.140948
obj = -17.614851, rho = -0.278832
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.126069
obj = -20.023285, rho = -0.324002
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*....*
optimization finished, #iter = 402
nu = 0.113314
obj = -22.742035, rho = -0.409533
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.098475
obj = -25.881256, rho = -0.416147
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*...*..............*
optimization finished, #iter = 1930
nu = 0.087069
obj = -29.666386, rho = -0.469432
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*
optimization finished, #iter = 285
nu = 0.077593
obj = -34.309109, rho = -0.434247
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*.*
optimization finished, #iter = 391
nu = 0.069515
obj = -39.812000, rho = -0.381758
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.064543
obj = -46.259435, rho = -0.438722
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.544852
obj = -3.740411, rho = -0.186291
nSV = 56, nBSV = 52
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.499545
obj = -4.252427, rho = -0.186454
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.439788
obj = -4.816283, rho = -0.232515
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.408869
obj = -5.411045, rho = -0.277706
nSV = 43, nBSV = 40
Total nSV = 43
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.358753
obj = -5.989409, rho = -0.268610
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.316477
obj = -6.597373, rho = -0.290210
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.273010
obj = -7.254869, rho = -0.283067
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.232467
obj = -7.988392, rho = -0.302001
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.198157
obj = -8.861337, rho = -0.297680
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.171337
obj = -9.922691, rho = -0.305714
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.151005
obj = -11.111530, rho = -0.294324
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.140000
obj = -12.340492, rho = -0.258274
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.126249
obj = -13.211713, rho = -0.264715
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 375
nu = 0.105435
obj = -14.039474, rho = -0.340642
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.......*
optimization finished, #iter = 966
nu = 0.088607
obj = -14.814718, rho = -0.383731
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*.....*
optimization finished, #iter = 844
nu = 0.071909
obj = -15.707855, rho = -0.382742
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.......*
optimization finished, #iter = 855
nu = 0.059550
obj = -16.810084, rho = -0.358548
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.050018
obj = -18.075202, rho = -0.321135
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.043859
obj = -19.291820, rho = -0.195002
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.039337
obj = -19.840512, rho = -0.041205
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.601077
obj = -4.000741, rho = -0.108547
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.528147
obj = -4.521757, rho = -0.119422
nSV = 54, nBSV = 50
Total nSV = 54
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.466871
obj = -5.133172, rho = -0.104975
nSV = 49, nBSV = 45
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.432125
obj = -5.782642, rho = -0.085681
nSV = 45, nBSV = 38
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.375932
obj = -6.463062, rho = -0.038418
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.331408
obj = -7.226327, rho = -0.045626
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.292566
obj = -8.051616, rho = -0.009333
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.255289
obj = -8.977707, rho = 0.000184
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.222623
obj = -10.035029, rho = -0.031568
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.199032
obj = -11.141986, rho = -0.056477
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.173526
obj = -12.304089, rho = -0.026274
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.150326
obj = -13.573106, rho = -0.013403
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.134217
obj = -14.904489, rho = 0.014557
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.117513
obj = -16.129439, rho = 0.014217
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.100071
obj = -17.276035, rho = 0.024367
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.084035
obj = -18.484876, rho = 0.058190
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*.*
optimization finished, #iter = 345
nu = 0.070351
obj = -19.741646, rho = 0.074931
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*.*
optimization finished, #iter = 336
nu = 0.057779
obj = -21.243999, rho = 0.068837
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 271
nu = 0.048604
obj = -22.969524, rho = 0.023944
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.041355
obj = -25.024099, rho = 0.001039
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.567250
obj = -3.798798, rho = -0.092721
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.500000
obj = -4.301608, rho = -0.122757
nSV = 51, nBSV = 49
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.457555
obj = -4.825882, rho = -0.134934
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.397735
obj = -5.421545, rho = -0.117319
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.353085
obj = -6.076533, rho = -0.130713
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.312377
obj = -6.808458, rho = -0.151133
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.272306
obj = -7.603769, rho = -0.168308
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.240438
obj = -8.493741, rho = -0.213904
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.209539
obj = -9.494762, rho = -0.201427
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.183681
obj = -10.646043, rho = -0.235631
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.160161
obj = -11.965255, rho = -0.303422
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.140482
obj = -13.492426, rho = -0.377883
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.124755
obj = -15.242797, rho = -0.378915
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.111232
obj = -17.184897, rho = -0.364329
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.103591
obj = -19.169885, rho = -0.238450
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.093207
obj = -20.866399, rho = -0.080525
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.078605
obj = -22.521172, rho = -0.042190
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.066879
obj = -24.180942, rho = -0.031995
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.055842
obj = -26.067098, rho = -0.086852
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 321
nu = 0.046772
obj = -28.222607, rho = -0.123890
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.579421
obj = -4.023414, rho = 0.094620
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.521707
obj = -4.594541, rho = 0.123212
nSV = 57, nBSV = 49
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.455724
obj = -5.282762, rho = 0.129911
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.412719
obj = -6.105005, rho = 0.115052
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.369003
obj = -7.080938, rho = 0.143400
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.339756
obj = -8.260196, rho = 0.161204
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.305082
obj = -9.611370, rho = 0.168888
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.281534
obj = -11.225984, rho = 0.042723
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.259288
obj = -13.087219, rho = -0.052374
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.238084
obj = -15.263000, rho = -0.115103
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.217622
obj = -17.747428, rho = -0.128016
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.199747
obj = -20.611255, rho = -0.096698
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.184408
obj = -23.801614, rho = -0.036938
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.165709
obj = -27.412890, rho = -0.041543
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.148235
obj = -31.587874, rho = -0.069498
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.134013
obj = -36.533232, rho = -0.113952
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.125462
obj = -42.174698, rho = -0.108702
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.118589
obj = -47.767351, rho = 0.008099
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.108226
obj = -52.945574, rho = 0.080055
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.094013
obj = -58.291451, rho = 0.104674
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.540760
obj = -3.691913, rho = -0.061358
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.488129
obj = -4.190044, rho = -0.150260
nSV = 51, nBSV = 44
Total nSV = 51
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.431995
obj = -4.756449, rho = -0.196464
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.388202
obj = -5.390856, rho = -0.245415
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.353791
obj = -6.063667, rho = -0.195193
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.314534
obj = -6.749390, rho = -0.133616
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.273482
obj = -7.476708, rho = -0.156995
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 215
nu = 0.234335
obj = -8.322512, rho = -0.162882
nSV = 31, nBSV = 19
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.205533
obj = -9.322706, rho = -0.185876
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.178139
obj = -10.477413, rho = -0.200851
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.155235
obj = -11.868263, rho = -0.239922
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.139678
obj = -13.417786, rho = -0.303556
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 185
nu = 0.122904
obj = -15.156052, rho = -0.350275
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.109219
obj = -17.212583, rho = -0.428604
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.101634
obj = -19.306764, rho = -0.536515
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 253
nu = 0.088568
obj = -21.380119, rho = -0.666677
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.075677
obj = -23.821753, rho = -0.741145
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.069142
obj = -26.488319, rho = -0.961010
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.062508
obj = -28.731724, rho = -1.211172
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.056733
obj = -30.273443, rho = -1.187267
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.603163
obj = -4.170569, rho = -0.144749
nSV = 62, nBSV = 59
Total nSV = 62
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.543184
obj = -4.761783, rho = -0.139088
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.488620
obj = -5.440695, rho = -0.164724
nSV = 53, nBSV = 47
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.440119
obj = -6.186763, rho = -0.231763
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.391377
obj = -7.030800, rho = -0.224469
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.355770
obj = -7.987017, rho = -0.222967
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.316920
obj = -8.980281, rho = -0.266705
nSV = 36, nBSV = 28
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.285042
obj = -10.074795, rho = -0.302855
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.252364
obj = -11.195457, rho = -0.236291
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.217686
obj = -12.433339, rho = -0.213944
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.187954
obj = -13.903010, rho = -0.193878
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.171611
obj = -15.520963, rho = -0.374082
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.155593
obj = -16.960815, rho = -0.500214
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.132156
obj = -18.217289, rho = -0.534146
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.111913
obj = -19.585247, rho = -0.552262
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.093748
obj = -21.041013, rho = -0.577422
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.078764
obj = -22.732063, rho = -0.594327
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.066883
obj = -24.648753, rho = -0.630608
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.056886
obj = -26.709132, rho = -0.672234
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.050716
obj = -28.667876, rho = -0.751739
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.518683
obj = -3.387769, rho = 0.049737
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.457480
obj = -3.784917, rho = 0.068559
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.403118
obj = -4.224695, rho = 0.103632
nSV = 42, nBSV = 38
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.361458
obj = -4.695720, rho = 0.074933
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.312615
obj = -5.174859, rho = 0.101819
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.270724
obj = -5.716997, rho = 0.100813
nSV = 29, nBSV = 24
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.231696
obj = -6.315299, rho = 0.116452
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.201811
obj = -7.030833, rho = 0.152533
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.173709
obj = -7.830788, rho = 0.183554
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.150817
obj = -8.780847, rho = 0.165769
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.137690
obj = -9.791362, rho = 0.112713
nSV = 16, nBSV = 12
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.126855
obj = -10.653057, rho = 0.126974
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.106192
obj = -11.382102, rho = 0.117067
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 295
nu = 0.089390
obj = -12.179339, rho = 0.095936
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 360
nu = 0.074068
obj = -13.099075, rho = 0.100468
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 429
nu = 0.061143
obj = -14.234670, rho = 0.100327
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.**.*
optimization finished, #iter = 164
nu = 0.051129
obj = -15.679741, rho = 0.099210
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.045977
obj = -17.340183, rho = 0.173600
nSV = 8, nBSV = 3
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.044217
obj = -18.448986, rho = 0.326389
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.036986
obj = -18.697309, rho = 0.406588
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.575668
obj = -3.952478, rho = -0.204393
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.521514
obj = -4.502305, rho = -0.169772
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.463226
obj = -5.096210, rho = -0.154517
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.416419
obj = -5.771536, rho = -0.180212
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.373312
obj = -6.524635, rho = -0.133122
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.331948
obj = -7.321886, rho = -0.125051
nSV = 36, nBSV = 30
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.296800
obj = -8.178928, rho = -0.070760
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.258884
obj = -9.114920, rho = -0.126439
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*...*.....*
optimization finished, #iter = 898
nu = 0.222159
obj = -10.194059, rho = -0.110978
nSV = 29, nBSV = 18
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*...*.*
optimization finished, #iter = 374
nu = 0.195321
obj = -11.483711, rho = -0.091663
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.170767
obj = -12.952519, rho = -0.118537
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.150463
obj = -14.687626, rho = -0.141699
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.132538
obj = -16.731996, rho = -0.172947
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.120833
obj = -19.070710, rho = -0.146403
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.112533
obj = -21.364723, rho = -0.143577
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.099621
obj = -23.554963, rho = -0.042871
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.086064
obj = -25.974899, rho = -0.081792
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.077307
obj = -28.403064, rho = -0.104084
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.067739
obj = -30.396944, rho = -0.193713
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.057233
obj = -32.217136, rho = -0.355287
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.558180
obj = -3.813520, rho = -0.054716
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.494325
obj = -4.351059, rho = -0.034335
nSV = 51, nBSV = 48
Total nSV = 51
Accuracy = 96% (96/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.445781
obj = -4.964665, rho = -0.080515
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.398219
obj = -5.649736, rho = -0.126862
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.355289
obj = -6.438740, rho = -0.165719
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.326630
obj = -7.299792, rho = -0.298369
nSV = 35, nBSV = 28
Total nSV = 35
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.288029
obj = -8.237460, rho = -0.256060
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.258729
obj = -9.255967, rho = -0.272886
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.225249
obj = -10.405842, rho = -0.242501
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.196892
obj = -11.777540, rho = -0.271742
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.171749
obj = -13.408748, rho = -0.286321
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.151457
obj = -15.421427, rho = -0.278713
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*...*
optimization finished, #iter = 435
nu = 0.139257
obj = -17.727356, rho = -0.232284
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.125776
obj = -20.324668, rho = -0.263405
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.114021
obj = -23.206461, rho = -0.350831
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.104805
obj = -26.255513, rho = -0.467314
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 384
nu = 0.091368
obj = -29.515737, rho = -0.500065
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.082403
obj = -33.371037, rho = -0.586352
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.071678
obj = -37.481848, rho = -0.599467
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.065203
obj = -42.278579, rho = -0.578219
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.543227
obj = -3.599480, rho = -0.146364
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.483673
obj = -4.039318, rho = -0.126195
nSV = 53, nBSV = 44
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.419493
obj = -4.540711, rho = -0.126106
nSV = 47, nBSV = 38
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.373421
obj = -5.132689, rho = -0.174418
nSV = 40, nBSV = 35
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.329576
obj = -5.772424, rho = -0.121161
nSV = 36, nBSV = 31
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.290159
obj = -6.503025, rho = -0.061159
nSV = 32, nBSV = 27
Total nSV = 32
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.261074
obj = -7.322240, rho = -0.018257
nSV = 28, nBSV = 24
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.229549
obj = -8.171256, rho = -0.060327
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.197191
obj = -9.199059, rho = -0.035912
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.177350
obj = -10.396515, rho = 0.037069
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.155609
obj = -11.745880, rho = 0.055007
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.135433
obj = -13.310804, rho = 0.062032
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.118620
obj = -15.227812, rho = 0.071526
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.105631
obj = -17.544281, rho = 0.061387
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.096360
obj = -20.269200, rho = 0.058030
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.089260
obj = -23.228343, rho = -0.018728
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.082312
obj = -26.303853, rho = -0.153515
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.072496
obj = -29.686619, rho = -0.163206
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.063090
obj = -33.669955, rho = -0.199111
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.056530
obj = -38.259374, rho = -0.317451
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.611082
obj = -4.249933, rho = -0.171296
nSV = 63, nBSV = 56
Total nSV = 63
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.548469
obj = -4.880427, rho = -0.150118
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.493341
obj = -5.606780, rho = -0.151666
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.441624
obj = -6.447772, rho = -0.129324
nSV = 48, nBSV = 42
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.401233
obj = -7.413700, rho = -0.154884
nSV = 43, nBSV = 37
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.359932
obj = -8.526166, rho = -0.102240
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.331726
obj = -9.755079, rho = 0.001162
nSV = 38, nBSV = 29
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.291302
obj = -11.162066, rho = 0.002863
nSV = 35, nBSV = 25
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.265215
obj = -12.868544, rho = 0.052226
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.246836
obj = -14.689340, rho = 0.111411
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.227033
obj = -16.452079, rho = 0.229321
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.203158
obj = -18.284377, rho = 0.316064
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 488
nu = 0.175236
obj = -20.207986, rho = 0.401343
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..**.*
optimization finished, #iter = 265
nu = 0.153338
obj = -22.332401, rho = 0.483649
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*
optimization finished, #iter = 256
nu = 0.131207
obj = -24.600950, rho = 0.564067
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*....*
optimization finished, #iter = 569
nu = 0.113757
obj = -27.258698, rho = 0.580044
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*.*
optimization finished, #iter = 409
nu = 0.102490
obj = -30.007539, rho = 0.639318
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*..*
optimization finished, #iter = 465
nu = 0.090807
obj = -32.496943, rho = 0.890452
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
....*..*
optimization finished, #iter = 697
nu = 0.075780
obj = -34.825609, rho = 0.989042
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
....*.............*
optimization finished, #iter = 1785
nu = 0.063202
obj = -37.457715, rho = 1.059217
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.562844
obj = -3.813324, rho = -0.086238
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.508149
obj = -4.324602, rho = -0.080923
nSV = 53, nBSV = 50
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.460865
obj = -4.857754, rho = -0.121926
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.410288
obj = -5.419713, rho = -0.151952
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.358621
obj = -6.014176, rho = -0.143213
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.312454
obj = -6.664491, rho = -0.113756
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.273400
obj = -7.393945, rho = -0.070372
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.238885
obj = -8.178897, rho = -0.090729
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.206762
obj = -9.001614, rho = -0.055602
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.177255
obj = -9.949803, rho = -0.029719
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.150230
obj = -11.068099, rho = -0.013550
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.132357
obj = -12.413572, rho = 0.048776
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.116069
obj = -13.866902, rho = 0.056827
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 196
nu = 0.104111
obj = -15.451428, rho = -0.052410
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.095214
obj = -16.961469, rho = -0.189951
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.082395
obj = -18.210924, rho = -0.312191
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.069323
obj = -19.553638, rho = -0.472705
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.059334
obj = -20.869695, rho = -0.491522
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.051365
obj = -22.006795, rho = -0.590947
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.044390
obj = -22.474842, rho = -0.593909
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.562710
obj = -3.718291, rho = -0.124099
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.507709
obj = -4.166418, rho = -0.113748
nSV = 53, nBSV = 49
Total nSV = 53
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.452409
obj = -4.621230, rho = -0.095934
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.393953
obj = -5.093697, rho = -0.094853
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.336427
obj = -5.615955, rho = -0.080201
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.291738
obj = -6.212090, rho = -0.098532
nSV = 34, nBSV = 25
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.250560
obj = -6.916551, rho = -0.113729
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.223673
obj = -7.658932, rho = -0.072002
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 99.4% (994/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.193498
obj = -8.443621, rho = -0.081657
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.170154
obj = -9.267496, rho = -0.067058
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.150387
obj = -10.133199, rho = 0.030887
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.128243
obj = -10.909559, rho = 0.062708
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.107635
obj = -11.798341, rho = 0.066571
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.**..*
optimization finished, #iter = 377
nu = 0.089594
obj = -12.826899, rho = 0.059221
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.077316
obj = -14.028877, rho = 0.028451
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.066315
obj = -15.298737, rho = 0.041898
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.057297
obj = -16.649178, rho = 0.072075
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.051001
obj = -17.933435, rho = 0.055028
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.044605
obj = -18.724783, rho = 0.010174
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.038075
obj = -19.037505, rho = 0.007410
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.548675
obj = -3.686260, rho = -0.183439
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.488377
obj = -4.169143, rho = -0.234426
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.429781
obj = -4.726240, rho = -0.231068
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.382646
obj = -5.366006, rho = -0.198094
nSV = 41, nBSV = 37
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.343552
obj = -6.072722, rho = -0.160780
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.313374
obj = -6.844574, rho = -0.251304
nSV = 32, nBSV = 28
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.277071
obj = -7.646571, rho = -0.327509
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.247318
obj = -8.490357, rho = -0.346767
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.217656
obj = -9.351700, rho = -0.304761
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.186921
obj = -10.276485, rho = -0.286085
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.159498
obj = -11.296840, rho = -0.275977
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.142327
obj = -12.431414, rho = -0.324375
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.124627
obj = -13.419900, rho = -0.390829
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.105026
obj = -14.410511, rho = -0.401041
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 167
nu = 0.091473
obj = -15.241756, rho = -0.363645
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.076318
obj = -16.084544, rho = -0.350454
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*...*
optimization finished, #iter = 460
nu = 0.063934
obj = -16.724943, rho = -0.359069
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.052751
obj = -17.316015, rho = -0.381223
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.044727
obj = -17.547218, rho = -0.395040
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.035100
obj = -17.547218, rho = -0.395040
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.573060
obj = -3.899000, rho = -0.322036
nSV = 59, nBSV = 54
Total nSV = 59
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.513509
obj = -4.425094, rho = -0.319377
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.461498
obj = -5.007497, rho = -0.306910
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.407513
obj = -5.655525, rho = -0.343643
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.359504
obj = -6.400151, rho = -0.378026
nSV = 39, nBSV = 30
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.320300
obj = -7.268399, rho = -0.476451
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.285027
obj = -8.225599, rho = -0.527713
nSV = 33, nBSV = 25
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.247651
obj = -9.361125, rho = -0.534607
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.222671
obj = -10.716479, rho = -0.631398
nSV = 28, nBSV = 18
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 174
nu = 0.196571
obj = -12.322623, rho = -0.669440
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.176986
obj = -14.207339, rho = -0.729452
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.158896
obj = -16.437236, rho = -0.761667
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.146458
obj = -18.998754, rho = -0.739454
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.135099
obj = -21.818949, rho = -0.891887
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.120153
obj = -24.988983, rho = -1.013164
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.107280
obj = -28.753926, rho = -1.100424
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.100000
obj = -33.023371, rho = -1.390885
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 111
nu = 0.096643
obj = -36.830688, rho = -1.869500
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.084284
obj = -40.328746, rho = -1.946434
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.072006
obj = -44.002307, rho = -2.003081
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.530536
obj = -3.680296, rho = 0.035514
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.471290
obj = -4.213787, rho = 0.053782
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.421948
obj = -4.853787, rho = 0.005540
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.392524
obj = -5.582787, rho = -0.101087
nSV = 42, nBSV = 37
Total nSV = 42
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.357509
obj = -6.344849, rho = -0.159794
nSV = 40, nBSV = 34
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.329383
obj = -7.127624, rho = -0.139756
nSV = 34, nBSV = 30
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.289853
obj = -7.918473, rho = -0.159870
nSV = 33, nBSV = 24
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.254653
obj = -8.802448, rho = -0.180762
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.223056
obj = -9.755979, rho = -0.241353
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.199645
obj = -10.644644, rho = -0.279736
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.172049
obj = -11.568202, rho = -0.213456
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.145297
obj = -12.535880, rho = -0.186705
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.125680
obj = -13.559276, rho = -0.253143
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.**.*
optimization finished, #iter = 177
nu = 0.105267
obj = -14.608543, rho = -0.326041
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*
optimization finished, #iter = 425
nu = 0.087422
obj = -15.844779, rho = -0.315003
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.073229
obj = -17.393603, rho = -0.308347
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.065289
obj = -19.079808, rho = -0.239508
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.058792
obj = -20.422798, rho = -0.083375
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.049676
obj = -21.361374, rho = -0.035281
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.041177
obj = -22.289715, rho = -0.100916
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.538356
obj = -3.679186, rho = -0.124780
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.485536
obj = -4.172013, rho = -0.097023
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.433462
obj = -4.721576, rho = -0.134574
nSV = 46, nBSV = 39
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.378093
obj = -5.368215, rho = -0.146773
nSV = 40, nBSV = 36
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.335966
obj = -6.129312, rho = -0.166211
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.299010
obj = -7.016357, rho = -0.158906
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.273221
obj = -8.037712, rho = -0.118434
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.253491
obj = -9.095291, rho = -0.059272
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.223464
obj = -10.226161, rho = -0.047913
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.201079
obj = -11.421385, rho = -0.140706
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.174005
obj = -12.721329, rho = -0.192036
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.149268
obj = -14.287004, rho = -0.190928
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.132580
obj = -16.078972, rho = -0.237526
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.118338
obj = -18.100881, rho = -0.340755
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.105329
obj = -20.231997, rho = -0.459259
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*...*
optimization finished, #iter = 322
nu = 0.092186
obj = -22.496881, rho = -0.577587
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 255
nu = 0.078507
obj = -25.245164, rho = -0.592886
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.070891
obj = -28.334138, rho = -0.717258
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.065933
obj = -31.216780, rho = -0.747798
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.059811
obj = -33.298017, rho = -0.766098
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.649484
obj = -4.499190, rho = -0.109014
nSV = 69, nBSV = 63
Total nSV = 69
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.578767
obj = -5.157170, rho = -0.097407
nSV = 61, nBSV = 55
Total nSV = 61
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.519177
obj = -5.929653, rho = -0.172365
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.464168
obj = -6.838497, rho = -0.155965
nSV = 49, nBSV = 44
Total nSV = 49
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.425974
obj = -7.884454, rho = -0.069363
nSV = 45, nBSV = 41
Total nSV = 45
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.392939
obj = -9.021401, rho = -0.005752
nSV = 41, nBSV = 38
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.353337
obj = -10.251005, rho = -0.005495
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.322336
obj = -11.575473, rho = -0.040767
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.280010
obj = -13.051876, rho = -0.045782
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.247984
obj = -14.773226, rho = -0.035140
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.220490
obj = -16.708809, rho = -0.048351
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.194609
obj = -18.915601, rho = -0.069767
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.172312
obj = -21.438850, rho = -0.090587
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.155485
obj = -24.311157, rho = -0.212810
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.136595
obj = -27.526069, rho = -0.273143
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.124422
obj = -31.174079, rho = -0.321036
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.112654
obj = -34.913056, rho = -0.273823
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*.*
optimization finished, #iter = 421
nu = 0.101433
obj = -38.448328, rho = -0.226168
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*.*
optimization finished, #iter = 446
nu = 0.085745
obj = -42.288037, rho = -0.208900
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*
optimization finished, #iter = 241
nu = 0.073565
obj = -46.832003, rho = -0.207590
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.601155
obj = -4.195555, rho = -0.058920
nSV = 62, nBSV = 58
Total nSV = 62
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.545291
obj = -4.788973, rho = -0.005735
nSV = 57, nBSV = 52
Total nSV = 57
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.485746
obj = -5.484385, rho = -0.002332
nSV = 51, nBSV = 47
Total nSV = 51
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.448501
obj = -6.249200, rho = -0.003555
nSV = 47, nBSV = 43
Total nSV = 47
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.399484
obj = -7.054872, rho = 0.052512
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.352285
obj = -7.992723, rho = 0.035324
nSV = 38, nBSV = 33
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.315023
obj = -9.049329, rho = -0.023023
nSV = 35, nBSV = 27
Total nSV = 35
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.273179
obj = -10.297082, rho = -0.029385
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.244512
obj = -11.789123, rho = 0.036143
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.222672
obj = -13.507622, rho = 0.023706
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.196918
obj = -15.404723, rho = 0.060162
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.173237
obj = -17.714457, rho = 0.023577
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.159749
obj = -20.411182, rho = 0.050061
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.140505
obj = -23.525624, rho = 0.085717
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 115
nu = 0.125850
obj = -27.313494, rho = 0.138961
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.116799
obj = -31.774311, rho = 0.208096
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.110103
obj = -36.556360, rho = 0.333567
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.097089
obj = -41.744037, rho = 0.396690
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.085232
obj = -48.137328, rho = 0.450132
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.075619
obj = -56.110784, rho = 0.481171
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.546385
obj = -3.831277, rho = -0.068739
nSV = 57, nBSV = 51
Total nSV = 57
Accuracy = 95% (95/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.491046
obj = -4.404392, rho = -0.048567
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 95% (95/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.441450
obj = -5.074369, rho = -0.084498
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 95% (95/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.393796
obj = -5.866361, rho = -0.106575
nSV = 43, nBSV = 35
Total nSV = 43
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.354525
obj = -6.819096, rho = -0.146787
nSV = 39, nBSV = 32
Total nSV = 39
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.320847
obj = -7.959393, rho = -0.193514
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.302762
obj = -9.234022, rho = -0.250413
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.269412
obj = -10.706009, rho = -0.235545
nSV = 32, nBSV = 24
Total nSV = 32
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.249120
obj = -12.432687, rho = -0.168094
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.225589
obj = -14.412461, rho = -0.117208
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.204363
obj = -16.734687, rho = -0.075211
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.184829
obj = -19.460390, rho = -0.086599
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.168314
obj = -22.763224, rho = -0.039753
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.156648
obj = -26.494973, rho = -0.020236
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.144858
obj = -30.679035, rho = -0.108640
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.130112
obj = -35.494328, rho = -0.148373
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.117730
obj = -41.154503, rho = -0.204544
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*...*
optimization finished, #iter = 629
nu = 0.110322
obj = -47.385628, rho = -0.308488
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 386
nu = 0.102495
obj = -53.914752, rho = -0.418243
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.089188
obj = -61.268042, rho = -0.416826
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.548559
obj = -3.724871, rho = 0.050663
nSV = 57, nBSV = 53
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.502377
obj = -4.214704, rho = -0.013407
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.440890
obj = -4.742826, rho = -0.053735
nSV = 47, nBSV = 42
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.392438
obj = -5.334486, rho = -0.094320
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.351647
obj = -5.975264, rho = -0.070098
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.309043
obj = -6.658392, rho = -0.027272
nSV = 33, nBSV = 28
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.271742
obj = -7.405462, rho = 0.042177
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.237431
obj = -8.213025, rho = 0.082096
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.207821
obj = -9.109735, rho = 0.036410
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.178615
obj = -10.077291, rho = -0.000093
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.156478
obj = -11.153718, rho = -0.005009
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.136444
obj = -12.341331, rho = 0.013578
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.117133
obj = -13.625430, rho = 0.012933
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.100007
obj = -15.200224, rho = 0.029244
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.087740
obj = -16.997834, rho = 0.053183
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.077159
obj = -19.009882, rho = 0.103518
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.068272
obj = -21.247024, rho = 0.066410
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.062251
obj = -23.548497, rho = 0.029608
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.056836
obj = -25.299671, rho = -0.016292
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.050321
obj = -26.498721, rho = -0.218457
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.587546
obj = -4.129786, rho = 0.000564
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.538805
obj = -4.742494, rho = -0.000768
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.487565
obj = -5.405539, rho = -0.078340
nSV = 52, nBSV = 46
Total nSV = 52
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.437486
obj = -6.146199, rho = -0.115827
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.396444
obj = -6.991884, rho = -0.195109
nSV = 44, nBSV = 36
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.349389
obj = -7.921628, rho = -0.179712
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.320000
obj = -8.950808, rho = -0.178636
nSV = 33, nBSV = 30
Total nSV = 33
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.282242
obj = -10.008133, rho = -0.221537
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.248498
obj = -11.174272, rho = -0.204290
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.216587
obj = -12.475460, rho = -0.194220
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.188026
obj = -13.963778, rho = -0.263151
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.168134
obj = -15.670806, rho = -0.152840
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.150673
obj = -17.407969, rho = -0.202872
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.132255
obj = -19.177718, rho = -0.178779
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*...*
optimization finished, #iter = 385
nu = 0.114044
obj = -21.101897, rho = -0.096942
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 227
nu = 0.097838
obj = -23.287657, rho = -0.037748
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.086970
obj = -25.567796, rho = -0.004951
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.077777
obj = -27.493105, rho = -0.045061
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.063561
obj = -29.423010, rho = -0.026649
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.053735
obj = -31.830298, rho = 0.025449
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.530885
obj = -3.499942, rho = 0.041134
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.471607
obj = -3.925848, rho = 0.035200
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.418561
obj = -4.393775, rho = 0.003234
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.367183
obj = -4.905334, rho = 0.010658
nSV = 39, nBSV = 35
Total nSV = 39
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.320366
obj = -5.456781, rho = 0.042763
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.286013
obj = -6.074221, rho = 0.040678
nSV = 30, nBSV = 25
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.247069
obj = -6.734533, rho = 0.040689
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.212372
obj = -7.492429, rho = 0.066082
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.187068
obj = -8.352293, rho = 0.097276
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.169472
obj = -9.208475, rho = 0.171069
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.143652
obj = -10.121104, rho = 0.212365
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.129351
obj = -11.065613, rho = 0.320190
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.113165
obj = -11.833349, rho = 0.320017
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.094168
obj = -12.578770, rho = 0.305988
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 198
nu = 0.077419
obj = -13.394140, rho = 0.281608
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.067064
obj = -14.231545, rho = 0.298569
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.057523
obj = -14.814310, rho = 0.301482
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.048152
obj = -15.055419, rho = 0.287773
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.038460
obj = -15.093073, rho = 0.315942
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.030182
obj = -15.093073, rho = 0.315942
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.593008
obj = -4.081903, rho = -0.029473
nSV = 60, nBSV = 58
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.541493
obj = -4.641939, rho = -0.069195
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.496843
obj = -5.215965, rho = -0.023402
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.431818
obj = -5.821074, rho = -0.033307
nSV = 49, nBSV = 40
Total nSV = 49
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.384089
obj = -6.482122, rho = 0.011384
nSV = 42, nBSV = 34
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.329734
obj = -7.224162, rho = 0.017760
nSV = 39, nBSV = 29
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.287482
obj = -8.120539, rho = -0.001923
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.255083
obj = -9.110345, rho = -0.113213
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.225406
obj = -10.192978, rho = -0.083033
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.203074
obj = -11.368200, rho = -0.096263
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.178551
obj = -12.496814, rho = -0.075065
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.155833
obj = -13.656107, rho = -0.131648
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.132703
obj = -14.890420, rho = -0.127911
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 509
nu = 0.118217
obj = -16.136222, rho = -0.190767
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*
optimization finished, #iter = 343
nu = 0.097141
obj = -17.403159, rho = -0.186825
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.086371
obj = -18.805975, rho = -0.268223
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.075437
obj = -19.713189, rho = -0.241412
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.060635
obj = -20.526102, rho = -0.237062
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.049273
obj = -21.476468, rho = -0.259234
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.041375
obj = -22.531129, rho = -0.160029
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.615057
obj = -4.125897, rho = -0.150878
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.550254
obj = -4.666071, rho = -0.185853
nSV = 58, nBSV = 50
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.486678
obj = -5.277712, rho = -0.167850
nSV = 51, nBSV = 46
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.427835
obj = -5.964141, rho = -0.198458
nSV = 47, nBSV = 41
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.380233
obj = -6.761675, rho = -0.200952
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.343369
obj = -7.625993, rho = -0.130540
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.298035
obj = -8.606099, rho = -0.114793
nSV = 36, nBSV = 26
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 155
nu = 0.264852
obj = -9.762104, rho = -0.157291
nSV = 31, nBSV = 23
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.232317
obj = -11.091421, rho = -0.180846
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.212741
obj = -12.635061, rho = -0.279112
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.190822
obj = -14.215196, rho = -0.317184
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.166156
obj = -16.050069, rho = -0.352750
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.151093
obj = -18.083077, rho = -0.366060
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.132407
obj = -20.233459, rho = -0.373929
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.117600
obj = -22.639762, rho = -0.363848
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.104976
obj = -25.213356, rho = -0.400084
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.091814
obj = -27.854202, rho = -0.415317
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.078947
obj = -30.784076, rho = -0.386315
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.069184
obj = -34.043749, rho = -0.319722
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.061746
obj = -37.333874, rho = -0.267187
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.554214
obj = -3.647386, rho = -0.145926
nSV = 57, nBSV = 54
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.493398
obj = -4.080888, rho = -0.134426
nSV = 53, nBSV = 45
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.434900
obj = -4.569901, rho = -0.175579
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.382242
obj = -5.099838, rho = -0.158604
nSV = 42, nBSV = 36
Total nSV = 42
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.330818
obj = -5.701318, rho = -0.151216
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.292358
obj = -6.350840, rho = -0.134774
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.258187
obj = -7.105321, rho = -0.154056
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.231042
obj = -7.849133, rho = -0.212232
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.201522
obj = -8.635373, rho = -0.257295
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.172971
obj = -9.469846, rho = -0.297462
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.149042
obj = -10.387140, rho = -0.300203
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.126776
obj = -11.432203, rho = -0.257472
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.116981
obj = -12.459667, rho = -0.286093
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.101010
obj = -13.199604, rho = -0.292346
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.............................................*
optimization finished, #iter = 4690
nu = 0.082655
obj = -13.955036, rho = -0.285234
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.................*
optimization finished, #iter = 1889
nu = 0.068934
obj = -14.712833, rho = -0.271361
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.057360
obj = -15.555623, rho = -0.273160
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.051564
obj = -16.064515, rho = -0.292926
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.040957
obj = -16.068400, rho = -0.295066
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.032141
obj = -16.068400, rho = -0.295066
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.588284
obj = -3.933428, rho = -0.252545
nSV = 62, nBSV = 56
Total nSV = 62
Accuracy = 96% (96/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.523210
obj = -4.443590, rho = -0.284030
nSV = 54, nBSV = 49
Total nSV = 54
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.462509
obj = -5.019472, rho = -0.294894
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 97% (97/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.408206
obj = -5.669705, rho = -0.267861
nSV = 45, nBSV = 37
Total nSV = 45
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.363338
obj = -6.422305, rho = -0.310689
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.318632
obj = -7.278205, rho = -0.333799
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.281413
obj = -8.297959, rho = -0.340068
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.250810
obj = -9.508774, rho = -0.287049
nSV = 29, nBSV = 23
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.226621
obj = -10.846796, rho = -0.238211
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.201469
obj = -12.416125, rho = -0.245631
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.181346
obj = -14.252716, rho = -0.291855
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.162619
obj = -16.346338, rho = -0.406402
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.146401
obj = -18.772571, rho = -0.459380
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.133189
obj = -21.556464, rho = -0.432617
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.118389
obj = -24.632364, rho = -0.332434
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 226
nu = 0.106699
obj = -28.329257, rho = -0.149564
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 316
nu = 0.094377
obj = -32.653722, rho = -0.104933
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.085932
obj = -37.898297, rho = -0.123664
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.077585
obj = -43.740031, rho = -0.127031
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.068291
obj = -51.012331, rho = -0.101628
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 32
nu = 0.540000
obj = -3.732784, rho = -0.201566
nSV = 55, nBSV = 53
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.488847
obj = -4.250498, rho = -0.177769
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.447402
obj = -4.823785, rho = -0.127559
nSV = 46, nBSV = 43
Total nSV = 46
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.398993
obj = -5.416487, rho = -0.054774
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.348019
obj = -6.093079, rho = -0.060782
nSV = 37, nBSV = 31
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.313193
obj = -6.829473, rho = 0.013122
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.275346
obj = -7.638218, rho = 0.033723
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.238469
obj = -8.546395, rho = 0.070348
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.222652
obj = -9.501922, rho = 0.013375
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.190664
obj = -10.402296, rho = -0.019201
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.163491
obj = -11.382451, rho = -0.037338
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.139287
obj = -12.499681, rho = -0.001438
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.122182
obj = -13.701497, rho = 0.034561
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.105191
obj = -14.929288, rho = -0.057677
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.091289
obj = -16.261065, rho = -0.171189
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.078184
obj = -17.503288, rho = -0.144718
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.065581
obj = -18.891965, rho = -0.133565
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.055581
obj = -20.433066, rho = -0.146647
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.050193
obj = -21.910739, rho = -0.227027
nSV = 8, nBSV = 3
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.044758
obj = -22.382584, rho = -0.337882
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.590410
obj = -3.892649, rho = -0.017500
nSV = 61, nBSV = 57
Total nSV = 61
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.523794
obj = -4.365074, rho = 0.017784
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.463930
obj = -4.885554, rho = 0.017819
nSV = 49, nBSV = 43
Total nSV = 49
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.399353
obj = -5.480067, rho = 0.002397
nSV = 45, nBSV = 36
Total nSV = 45
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.351208
obj = -6.168231, rho = 0.024751
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.318058
obj = -6.935719, rho = -0.006221
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.275680
obj = -7.770216, rho = 0.030488
nSV = 32, nBSV = 25
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.244653
obj = -8.722230, rho = 0.075901
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.216502
obj = -9.763086, rho = 0.058979
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.188787
obj = -10.901171, rho = 0.005899
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.165920
obj = -12.199161, rho = 0.001071
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.142093
obj = -13.734487, rho = -0.021851
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.124300
obj = -15.621928, rho = -0.002474
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.110683
obj = -17.856638, rho = 0.032306
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.102040
obj = -20.290969, rho = 0.064019
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.090644
obj = -22.963090, rho = 0.004828
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.083166
obj = -25.743825, rho = -0.065869
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.074355
obj = -28.492277, rho = -0.010672
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.067220
obj = -30.909558, rho = 0.061926
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 179
nu = 0.056420
obj = -33.151724, rho = 0.071838
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.543747
obj = -3.523142, rho = -0.086800
nSV = 58, nBSV = 52
Total nSV = 58
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.476999
obj = -3.933820, rho = -0.123675
nSV = 50, nBSV = 45
Total nSV = 50
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.424873
obj = -4.373332, rho = -0.146673
nSV = 46, nBSV = 41
Total nSV = 46
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.370825
obj = -4.823561, rho = -0.167484
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.327722
obj = -5.316314, rho = -0.222927
nSV = 35, nBSV = 29
Total nSV = 35
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.283197
obj = -5.791967, rho = -0.258629
nSV = 32, nBSV = 23
Total nSV = 32
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.238943
obj = -6.327900, rho = -0.256672
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.209637
obj = -6.910010, rho = -0.234640
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.180219
obj = -7.503978, rho = -0.267146
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 250
nu = 0.151575
obj = -8.154832, rho = -0.231033
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*.*
optimization finished, #iter = 197
nu = 0.127973
obj = -8.913550, rho = -0.285239
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.117729
obj = -9.636409, rho = -0.431091
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.099475
obj = -10.111166, rho = -0.474579
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.081843
obj = -10.616218, rho = -0.488559
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.068484
obj = -11.126536, rho = -0.441276
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.058261
obj = -11.470981, rho = -0.465165
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*....*
optimization finished, #iter = 548
nu = 0.046845
obj = -11.663265, rho = -0.484873
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.037313
obj = -11.829576, rho = -0.491892
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 314
nu = 0.030369
obj = -11.917989, rho = -0.499172
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 314
nu = 0.023832
obj = -11.917989, rho = -0.499172
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.549866
obj = -3.600033, rho = -0.243344
nSV = 57, nBSV = 50
Total nSV = 57
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.493472
obj = -4.005649, rho = -0.181959
nSV = 53, nBSV = 46
Total nSV = 53
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.422084
obj = -4.463774, rho = -0.177900
nSV = 47, nBSV = 40
Total nSV = 47
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.377744
obj = -4.969148, rho = -0.110083
nSV = 41, nBSV = 35
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.331213
obj = -5.490756, rho = -0.070961
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.286802
obj = -6.079193, rho = -0.078449
nSV = 31, nBSV = 26
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.247870
obj = -6.730118, rho = -0.103467
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 21
nu = 0.220000
obj = -7.427902, rho = -0.146749
nSV = 24, nBSV = 21
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.189710
obj = -8.127158, rho = -0.174163
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.163618
obj = -8.879851, rho = -0.196074
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.143891
obj = -9.690692, rho = -0.230003
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 236
nu = 0.125689
obj = -10.367050, rho = -0.280486
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*...*
optimization finished, #iter = 404
nu = 0.105674
obj = -11.061577, rho = -0.291149
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*....*
optimization finished, #iter = 472
nu = 0.089467
obj = -11.642221, rho = -0.311507
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*........*
optimization finished, #iter = 953
nu = 0.072595
obj = -12.290549, rho = -0.305060
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.060121
obj = -13.045566, rho = -0.253870
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.049205
obj = -13.930201, rho = -0.246609
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.040501
obj = -15.044220, rho = -0.241392
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.034173
obj = -16.429279, rho = -0.257622
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.030143
obj = -17.884671, rho = -0.316708
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.588078
obj = -3.860148, rho = 0.093994
nSV = 62, nBSV = 55
Total nSV = 62
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.526774
obj = -4.332385, rho = 0.038570
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.462165
obj = -4.822162, rho = 0.013621
nSV = 50, nBSV = 43
Total nSV = 50
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.401464
obj = -5.376660, rho = 0.046401
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.353348
obj = -5.999685, rho = 0.051891
nSV = 38, nBSV = 32
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.312896
obj = -6.681445, rho = 0.040141
nSV = 34, nBSV = 28
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.276755
obj = -7.378760, rho = 0.073306
nSV = 31, nBSV = 22
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.233348
obj = -8.159083, rho = 0.078984
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.199744
obj = -9.118060, rho = 0.049452
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.176482
obj = -10.234238, rho = 0.102501
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.153770
obj = -11.509841, rho = 0.089783
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 219
nu = 0.133425
obj = -13.006588, rho = 0.091789
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.118449
obj = -14.817262, rho = 0.036496
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.107166
obj = -16.791999, rho = 0.003096
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*...*
optimization finished, #iter = 318
nu = 0.092436
obj = -19.095629, rho = 0.021633
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.082955
obj = -21.932189, rho = -0.012478
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.077472
obj = -24.967272, rho = -0.111679
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 324
nu = 0.070009
obj = -28.016422, rho = -0.280388
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.060325
obj = -31.492157, rho = -0.326198
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.052613
obj = -35.720953, rho = -0.313196
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.580048
obj = -3.928970, rho = -0.029208
nSV = 60, nBSV = 55
Total nSV = 60
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.514843
obj = -4.461737, rho = -0.027604
nSV = 55, nBSV = 49
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.458785
obj = -5.070969, rho = 0.000049
nSV = 50, nBSV = 44
Total nSV = 50
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.415592
obj = -5.738961, rho = -0.073025
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.368772
obj = -6.484082, rho = -0.049800
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.326539
obj = -7.304170, rho = -0.043580
nSV = 38, nBSV = 28
Total nSV = 38
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.284706
obj = -8.267949, rho = -0.044693
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.252711
obj = -9.396909, rho = -0.042193
nSV = 30, nBSV = 21
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.228224
obj = -10.691239, rho = -0.004882
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.204640
obj = -12.039994, rho = -0.010817
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.178870
obj = -13.612690, rho = -0.055066
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 112
nu = 0.155911
obj = -15.485310, rho = -0.022663
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.138376
obj = -17.754291, rho = -0.055816
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.125939
obj = -20.336089, rho = -0.051826
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.115332
obj = -23.175933, rho = 0.020726
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.104326
obj = -26.161428, rho = 0.063830
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.093628
obj = -29.408844, rho = 0.128545
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.088154
obj = -32.433449, rho = -0.009431
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.078513
obj = -34.454068, rho = -0.245395
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.068065
obj = -35.863222, rho = -0.557581
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.553599
obj = -3.866720, rho = -0.147132
nSV = 56, nBSV = 54
Total nSV = 56
Accuracy = 96% (96/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.491877
obj = -4.448108, rho = -0.108119
nSV = 54, nBSV = 48
Total nSV = 54
Accuracy = 95% (95/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.442436
obj = -5.131763, rho = -0.100670
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 95% (95/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.405512
obj = -5.919359, rho = -0.076153
nSV = 43, nBSV = 38
Total nSV = 43
Accuracy = 95% (95/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.366585
obj = -6.789517, rho = -0.106434
nSV = 40, nBSV = 32
Total nSV = 40
Accuracy = 95% (95/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.326731
obj = -7.833353, rho = -0.135488
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 93% (93/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.289162
obj = -9.098019, rho = -0.155516
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 93% (93/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.260593
obj = -10.667765, rho = -0.153923
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 95% (95/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.236327
obj = -12.581163, rho = -0.130724
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 95% (95/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.215603
obj = -14.933182, rho = -0.112552
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 96% (96/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.199319
obj = -17.824842, rho = -0.109559
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.191767
obj = -21.261054, rho = -0.223410
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.185212
obj = -25.016649, rho = -0.346303
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.171809
obj = -29.116718, rho = -0.279559
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 264
nu = 0.154054
obj = -33.991748, rho = -0.233970
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 313
nu = 0.140309
obj = -39.888110, rho = -0.141912
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*..*
optimization finished, #iter = 331
nu = 0.128574
obj = -46.935009, rho = -0.130472
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*.....*
optimization finished, #iter = 709
nu = 0.119509
obj = -55.211773, rho = -0.148071
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*..*
optimization finished, #iter = 344
nu = 0.112332
obj = -64.448069, rho = -0.307036
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.101763
obj = -75.332079, rho = -0.415236
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.613319
obj = -4.255798, rho = -0.069522
nSV = 64, nBSV = 59
Total nSV = 64
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.551071
obj = -4.866341, rho = -0.079125
nSV = 58, nBSV = 53
Total nSV = 58
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.500000
obj = -5.566126, rho = -0.076431
nSV = 52, nBSV = 49
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.460000
obj = -6.309017, rho = -0.144257
nSV = 48, nBSV = 44
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.405616
obj = -7.099723, rho = -0.144116
nSV = 44, nBSV = 38
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.358722
obj = -8.006965, rho = -0.148485
nSV = 39, nBSV = 31
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.323403
obj = -8.958619, rho = -0.247914
nSV = 36, nBSV = 27
Total nSV = 36
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.281634
obj = -9.993702, rho = -0.256564
nSV = 34, nBSV = 24
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.....*
optimization finished, #iter = 520
nu = 0.244965
obj = -11.172486, rho = -0.299569
nSV = 29, nBSV = 20
Total nSV = 29
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.214947
obj = -12.570127, rho = -0.337487
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.187131
obj = -14.173265, rho = -0.215301
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.165697
obj = -16.057978, rho = -0.246216
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.143585
obj = -18.284659, rho = -0.282922
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.130563
obj = -20.918903, rho = -0.379856
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.123016
obj = -23.632807, rho = -0.333496
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.108124
obj = -26.271834, rho = -0.325227
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.091715
obj = -29.414757, rho = -0.298696
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.079363
obj = -33.356516, rho = -0.290742
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.075244
obj = -37.533128, rho = -0.495404
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.068133
obj = -41.021420, rho = -0.662368
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.558158
obj = -3.713288, rho = -0.027438
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.487841
obj = -4.196681, rho = -0.045948
nSV = 54, nBSV = 46
Total nSV = 54
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.440000
obj = -4.748882, rho = -0.115334
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.384610
obj = -5.371391, rho = -0.106111
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.341023
obj = -6.101168, rho = -0.114118
nSV = 37, nBSV = 29
Total nSV = 37
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.302405
obj = -6.955608, rho = -0.086081
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.268954
obj = -7.947340, rho = -0.046377
nSV = 31, nBSV = 25
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.244483
obj = -9.034385, rho = 0.010807
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.217007
obj = -10.276683, rho = 0.051258
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.190211
obj = -11.723143, rho = 0.087632
nSV = 22, nBSV = 17
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.176342
obj = -13.356234, rho = 0.155073
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.162545
obj = -14.993415, rho = 0.236657
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*.*
optimization finished, #iter = 208
nu = 0.143943
obj = -16.656363, rho = 0.234250
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.126163
obj = -18.406900, rho = 0.132365
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.108785
obj = -20.266047, rho = 0.108631
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.091312
obj = -22.498575, rho = 0.111862
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.082029
obj = -25.127522, rho = 0.168494
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.076165
obj = -27.434714, rho = 0.206023
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 342
nu = 0.064453
obj = -29.243322, rho = 0.247461
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*....*...*
optimization finished, #iter = 985
nu = 0.054030
obj = -31.178557, rho = 0.287104
nSV = 13, nBSV = 1
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.626973
obj = -4.509940, rho = -0.253803
nSV = 65, nBSV = 61
Total nSV = 65
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.580000
obj = -5.217246, rho = -0.266227
nSV = 60, nBSV = 56
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.540631
obj = -5.973188, rho = -0.206568
nSV = 56, nBSV = 51
Total nSV = 56
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.486275
obj = -6.780482, rho = -0.207549
nSV = 52, nBSV = 47
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.432704
obj = -7.661158, rho = -0.203387
nSV = 45, nBSV = 40
Total nSV = 45
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.382067
obj = -8.694292, rho = -0.223341
nSV = 41, nBSV = 36
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.345134
obj = -9.844227, rho = -0.240221
nSV = 37, nBSV = 32
Total nSV = 37
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.309281
obj = -11.079909, rho = -0.286366
nSV = 34, nBSV = 29
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.279014
obj = -12.367021, rho = -0.298107
nSV = 30, nBSV = 24
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.244328
obj = -13.685408, rho = -0.295730
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.212938
obj = -15.126596, rho = -0.284901
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.181444
obj = -16.783049, rho = -0.275073
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.160151
obj = -18.713432, rho = -0.367064
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.141313
obj = -20.706304, rho = -0.462829
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.127924
obj = -22.589195, rho = -0.593849
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.108629
obj = -24.381071, rho = -0.522159
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 299
nu = 0.094975
obj = -26.042840, rho = -0.653798
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.078733
obj = -27.595081, rho = -0.791435
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
....*..*
optimization finished, #iter = 691
nu = 0.067261
obj = -28.979833, rho = -0.902537
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*...*
optimization finished, #iter = 510
nu = 0.054358
obj = -30.376303, rho = -0.909044
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.537478
obj = -3.434511, rho = -0.016908
nSV = 57, nBSV = 49
Total nSV = 57
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.473612
obj = -3.806589, rho = 0.054388
nSV = 51, nBSV = 45
Total nSV = 51
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.411597
obj = -4.216023, rho = 0.053809
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.359124
obj = -4.645352, rho = 0.036389
nSV = 41, nBSV = 33
Total nSV = 41
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.310735
obj = -5.129453, rho = 0.050018
nSV = 33, nBSV = 27
Total nSV = 33
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.268239
obj = -5.645214, rho = 0.063978
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.229747
obj = -6.245588, rho = 0.036375
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.198410
obj = -6.930451, rho = 0.015027
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.174840
obj = -7.690365, rho = -0.005168
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 180
nu = 0.151375
obj = -8.527119, rho = -0.021764
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.131256
obj = -9.476386, rho = -0.069818
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.113465
obj = -10.557294, rho = -0.037799
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.098000
obj = -11.838283, rho = 0.002450
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.087333
obj = -13.283531, rho = 0.088070
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.079502
obj = -14.763209, rho = 0.170465
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.069865
obj = -16.157816, rho = 0.237124
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.060927
obj = -17.564778, rho = 0.280473
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.053652
obj = -18.850622, rho = 0.296254
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.046033
obj = -19.878113, rho = 0.359732
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 313
nu = 0.039888
obj = -20.486250, rho = 0.409672
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.560631
obj = -3.773059, rho = -0.140507
nSV = 60, nBSV = 54
Total nSV = 60
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.496429
obj = -4.273343, rho = -0.126973
nSV = 52, nBSV = 48
Total nSV = 52
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.449106
obj = -4.826562, rho = -0.132761
nSV = 46, nBSV = 42
Total nSV = 46
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.394251
obj = -5.439508, rho = -0.081459
nSV = 43, nBSV = 36
Total nSV = 43
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.346990
obj = -6.155328, rho = -0.056960
nSV = 39, nBSV = 33
Total nSV = 39
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 196
nu = 0.312294
obj = -6.935967, rho = -0.020859
nSV = 34, nBSV = 27
Total nSV = 34
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.280000
obj = -7.813781, rho = 0.013789
nSV = 30, nBSV = 27
Total nSV = 30
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.249221
obj = -8.721683, rho = 0.045592
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.216531
obj = -9.723131, rho = 0.057658
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.188130
obj = -10.858657, rho = 0.063404
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.162095
obj = -12.220930, rho = 0.076860
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.146777
obj = -13.746551, rho = 0.096626
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.135714
obj = -15.222819, rho = 0.028401
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.122808
obj = -16.416085, rho = -0.015016
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.101169
obj = -17.510704, rho = -0.012156
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*...*
optimization finished, #iter = 564
nu = 0.086604
obj = -18.604087, rho = -0.049418
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
...*..*
optimization finished, #iter = 594
nu = 0.070971
obj = -19.787379, rho = -0.065118
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*......*
optimization finished, #iter = 701
nu = 0.059205
obj = -21.127569, rho = -0.082342
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 253
nu = 0.049536
obj = -22.668622, rho = -0.107300
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.042887
obj = -24.131177, rho = -0.050476
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.578097
obj = -3.929299, rho = -0.091021
nSV = 61, nBSV = 56
Total nSV = 61
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.524649
obj = -4.454167, rho = -0.130644
nSV = 55, nBSV = 50
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.467500
obj = -5.008201, rho = -0.124781
nSV = 51, nBSV = 42
Total nSV = 51
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.418414
obj = -5.625804, rho = -0.055110
nSV = 43, nBSV = 39
Total nSV = 43
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.372724
obj = -6.261825, rho = -0.048003
nSV = 41, nBSV = 34
Total nSV = 41
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.331483
obj = -6.910793, rho = -0.097420
nSV = 36, nBSV = 29
Total nSV = 36
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.282833
obj = -7.623198, rho = -0.103611
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.254227
obj = -8.402352, rho = -0.030745
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.217541
obj = -9.130427, rho = 0.009619
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.184900
obj = -9.931959, rho = 0.042476
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.166269
obj = -10.721912, rho = -0.105760
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.140969
obj = -11.329624, rho = -0.131378
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 295
nu = 0.116377
obj = -11.919966, rho = -0.090118
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.096365
obj = -12.527919, rho = -0.112028
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 457
nu = 0.078817
obj = -13.197744, rho = -0.145494
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.067495
obj = -13.880403, rho = -0.062186
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.054870
obj = -14.412806, rho = -0.021631
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.045266
obj = -14.974353, rho = 0.055656
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.037659
obj = -15.334390, rho = 0.155561
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.030494
obj = -15.601830, rho = 0.115037
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.564667
obj = -3.879609, rho = -0.092367
nSV = 59, nBSV = 53
Total nSV = 59
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.500896
obj = -4.435864, rho = -0.067622
nSV = 55, nBSV = 46
Total nSV = 55
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.441703
obj = -5.103621, rho = -0.053977
nSV = 48, nBSV = 43
Total nSV = 48
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.406299
obj = -5.881609, rho = -0.012814
nSV = 44, nBSV = 40
Total nSV = 44
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.373327
obj = -6.727152, rho = -0.038989
nSV = 39, nBSV = 36
Total nSV = 39
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.342420
obj = -7.601573, rho = 0.066760
nSV = 38, nBSV = 31
Total nSV = 38
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.304626
obj = -8.541671, rho = 0.084930
nSV = 33, nBSV = 26
Total nSV = 33
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.267570
obj = -9.566762, rho = 0.054006
nSV = 31, nBSV = 24
Total nSV = 31
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.232539
obj = -10.747175, rho = 0.021426
nSV = 28, nBSV = 22
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.203583
obj = -12.125552, rho = 0.069381
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.181437
obj = -13.764179, rho = 0.124849
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.168601
obj = -15.421498, rho = 0.082545
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.147119
obj = -17.098866, rho = 0.012801
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.128630
obj = -18.852845, rho = -0.078502
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*..*
optimization finished, #iter = 375
nu = 0.112015
obj = -20.722583, rho = -0.035075
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.099555
obj = -22.600354, rho = -0.098112
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.085054
obj = -24.438932, rho = -0.082783
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.071905
obj = -26.434064, rho = -0.132896
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 351
nu = 0.060654
obj = -28.684254, rho = -0.218991
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.051641
obj = -31.165318, rho = -0.307785
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.520000
obj = -3.514528, rho = -0.168534
nSV = 55, nBSV = 51
Total nSV = 55
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 25
nu = 0.464138
obj = -3.981207, rho = -0.215138
nSV = 48, nBSV = 46
Total nSV = 48
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.423135
obj = -4.485843, rho = -0.208470
nSV = 44, nBSV = 39
Total nSV = 44
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.375510
obj = -5.012786, rho = -0.149923
nSV = 40, nBSV = 33
Total nSV = 40
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.324709
obj = -5.614481, rho = -0.191447
nSV = 37, nBSV = 30
Total nSV = 37
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.290615
obj = -6.306227, rho = -0.242043
nSV = 30, nBSV = 26
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.251671
obj = -7.050572, rho = -0.204931
nSV = 30, nBSV = 22
Total nSV = 30
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.220488
obj = -7.899226, rho = -0.156641
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 26
nu = 0.198820
obj = -8.874888, rho = -0.213776
nSV = 21, nBSV = 18
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.179192
obj = -9.769118, rho = -0.203241
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.155871
obj = -10.687263, rho = -0.230823
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.135978
obj = -11.603449, rho = -0.308501
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.114874
obj = -12.517105, rho = -0.346174
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.097408
obj = -13.564327, rho = -0.428449
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.084997
obj = -14.573436, rho = -0.436928
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.071938
obj = -15.493243, rho = -0.432280
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.059010
obj = -16.450390, rho = -0.427360
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.048715
obj = -17.618253, rho = -0.445880
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.040695
obj = -18.985377, rho = -0.478730
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.034183
obj = -20.541190, rho = -0.496419
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
No description has been provided for this image
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt


def data(N,sigma):   
    w = np.ones(10)/np.sqrt(10)   
    w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)   
    w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)   
    x = np.zeros((4,10))   
    x[1,:] = x[0,:] + sigma*w1   
    x[2,:] = x[0,:] + sigma*w2   
    x[3,:] = x[2,:] + sigma*w1   
    X1 = x + sigma*matlib.repmat(w,4,1)/2   
    X2 = x - sigma*matlib.repmat(w,4,1)/2   
    X1 = matlib.repmat(X1,2*N,1)   
    X2 = matlib.repmat(X2,2*N,1)   
    X = np.concatenate((X1, X2), axis=0)   
    Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)   
    Z = np.random.permutation(16*N)   
    Z = Z[:N]   
    X = X[Z,:]   
    X = X + 0.2*sigma*np.random.randn(N,10)   
    Y = Y[Z]

    return X, Y

# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-1, 1, 20)  # Logarithmically spaced values between 0.01 and 10

# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 3

# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):

    # Generate data
    X_train, y_train = data(100,sigma)
    X_test, y_test = data(1000, sigma)

    for lam in lambda_values:
        
        # Train SVM
        svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
        param = svm_parameter(f'-t 0 -c {lam}')
        model = svm_train(svm_problem_setup, param)
        
        # Predict on training and test data
        i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
        i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
        
        # Calculate errors
        training_errors.append(100 - train_accuracy[0])  # Convert to error percentage
        test_errors.append(100 - test_accuracy[0])  # Convert to error percentage

# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)

avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)

lambda_values_log = np.log10(lambda_values)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')

plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.1,10)@ σ = 3')
plt.legend()
plt.show()
*
optimization finished, #iter = 38
nu = 0.190142
obj = -1.211850, rho = 0.037704
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.168626
obj = -1.335853, rho = 0.154344
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.142376
obj = -1.475279, rho = 0.175642
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.124389
obj = -1.630865, rho = 0.163166
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.108915
obj = -1.811548, rho = 0.154842
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.092236
obj = -2.015882, rho = 0.140347
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.085309
obj = -2.234035, rho = 0.262038
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.073788
obj = -2.423210, rho = 0.428452
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.065300
obj = -2.596418, rho = 0.435621
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.054145
obj = -2.759100, rho = 0.433550
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*.....*
optimization finished, #iter = 769
nu = 0.044495
obj = -2.953266, rho = 0.434108
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.039693
obj = -3.140660, rho = 0.634223
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.034888
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.027379
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.021486
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.016861
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.013232
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.010384
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.008149
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.006395
obj = -3.197255, rho = 0.796246
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.175712
obj = -1.081133, rho = 0.335509
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.155307
obj = -1.173172, rho = 0.411492
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.133540
obj = -1.267691, rho = 0.437548
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*..*
optimization finished, #iter = 283
nu = 0.116267
obj = -1.336878, rho = 0.484385
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.095821
obj = -1.406824, rho = 0.487147
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.080776
obj = -1.466724, rho = 0.484916
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.067614
obj = -1.503268, rho = 0.552078
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.054624
obj = -1.528397, rho = 0.514669
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.044225
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.034706
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.027236
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.021373
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.016773
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.013163
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.010330
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.008106
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.006361
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.004992
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.003918
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.003074
obj = -1.537308, rho = 0.469606
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.173593
obj = -1.160380, rho = -0.165652
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.152592
obj = -1.315909, rho = -0.165012
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.139118
obj = -1.482378, rho = -0.066433
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.122086
obj = -1.663047, rho = -0.055474
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.107640
obj = -1.866270, rho = -0.184768
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 174
nu = 0.094478
obj = -2.100472, rho = -0.138851
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.084317
obj = -2.360544, rho = -0.045985
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.077725
obj = -2.618764, rho = 0.111645
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.068395
obj = -2.850873, rho = 0.211199
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.060196
obj = -3.063973, rho = 0.284546
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.053057
obj = -3.216588, rho = 0.353689
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 343
nu = 0.042487
obj = -3.338393, rho = 0.353893
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*....*
optimization finished, #iter = 545
nu = 0.035146
obj = -3.466880, rho = 0.337154
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.029733
obj = -3.566059, rho = 0.287629
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.023994
obj = -3.571136, rho = 0.254953
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.018830
obj = -3.571136, rho = 0.254953
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.014777
obj = -3.571136, rho = 0.254953
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.011596
obj = -3.571136, rho = 0.254953
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.009100
obj = -3.571136, rho = 0.254953
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.007141
obj = -3.571136, rho = 0.254953
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.249291
obj = -1.708948, rho = -0.180394
nSV = 29, nBSV = 21
Total nSV = 29
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.224763
obj = -1.944262, rho = -0.132052
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.204348
obj = -2.198305, rho = -0.147212
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.179477
obj = -2.473719, rho = -0.154872
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.159024
obj = -2.790455, rho = -0.235622
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.142783
obj = -3.146709, rho = -0.328538
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 336
nu = 0.123386
obj = -3.535789, rho = -0.361930
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 309
nu = 0.107453
obj = -4.023285, rho = -0.365891
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 313
nu = 0.093839
obj = -4.611735, rho = -0.353565
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*.*
optimization finished, #iter = 487
nu = 0.082198
obj = -5.346888, rho = -0.352906
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.073965
obj = -6.276517, rho = -0.339983
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.069729
obj = -7.361163, rho = -0.309775
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.065347
obj = -8.532157, rho = -0.328318
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.059641
obj = -9.850531, rho = -0.371385
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 350
nu = 0.055435
obj = -11.287150, rho = -0.351422
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.050805
obj = -12.833200, rho = -0.295450
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.048525
obj = -14.255105, rho = -0.265319
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*
optimization finished, #iter = 232
nu = 0.046739
obj = -15.101266, rho = -0.227109
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
....*........*
optimization finished, #iter = 1269
nu = 0.038617
obj = -15.153419, rho = -0.206516
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
....*........*
optimization finished, #iter = 1269
nu = 0.030305
obj = -15.153419, rho = -0.206516
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.187273
obj = -1.335359, rho = -0.014571
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.168301
obj = -1.547234, rho = -0.049823
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.151325
obj = -1.800956, rho = -0.072736
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.138608
obj = -2.102194, rho = -0.059102
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.128927
obj = -2.451340, rho = -0.066736
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.117136
obj = -2.833913, rho = -0.021689
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.104989
obj = -3.300741, rho = -0.035129
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.096628
obj = -3.838569, rho = 0.023255
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.089095
obj = -4.457789, rho = 0.075214
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.078578
obj = -5.200102, rho = 0.052191
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.070778
obj = -6.133355, rho = 0.007236
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.064047
obj = -7.298998, rho = 0.012960
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.062285
obj = -8.685966, rho = -0.022389
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.057633
obj = -10.211676, rho = -0.045127
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.052225
obj = -12.101779, rho = -0.054145
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.048517
obj = -14.432139, rho = -0.041179
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.047922
obj = -17.047651, rho = 0.207391
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.046952
obj = -19.623858, rho = 0.578223
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.044472
obj = -22.024924, rho = 0.698877
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
....*.*
optimization finished, #iter = 574
nu = 0.041991
obj = -23.859942, rho = 0.782367
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.171143
obj = -1.133502, rho = -0.180772
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.151269
obj = -1.278264, rho = -0.250186
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.131107
obj = -1.444885, rho = -0.268659
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.113946
obj = -1.651005, rho = -0.305198
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
....*...*
optimization finished, #iter = 739
nu = 0.099347
obj = -1.909732, rho = -0.305928
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
....*...*
optimization finished, #iter = 728
nu = 0.087980
obj = -2.237336, rho = -0.311764
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*
optimization finished, #iter = 375
nu = 0.079228
obj = -2.651778, rho = -0.337154
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.072516
obj = -3.170408, rho = -0.371413
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.068240
obj = -3.805756, rho = -0.456679
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.063265
obj = -4.585348, rho = -0.466610
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 240
nu = 0.060539
obj = -5.527097, rho = -0.663144
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.055624
obj = -6.683090, rho = -0.632158
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 272
nu = 0.052137
obj = -8.149045, rho = -0.607920
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.050006
obj = -9.971047, rho = -0.671862
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.048993
obj = -12.149611, rho = -0.928772
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.045701
obj = -14.791138, rho = -0.924287
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 387
nu = 0.043145
obj = -18.135940, rho = -0.863235
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.041118
obj = -22.345253, rho = -0.901442
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
....*.*
optimization finished, #iter = 505
nu = 0.039977
obj = -27.556118, rho = -1.188709
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.....*..*
optimization finished, #iter = 730
nu = 0.038023
obj = -34.041136, rho = -1.347543
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.212541
obj = -1.506523, rho = -0.124411
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.198440
obj = -1.730955, rho = -0.075045
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.183197
obj = -1.953831, rho = -0.058009
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.162013
obj = -2.197747, rho = -0.108115
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.147533
obj = -2.446874, rho = -0.163820
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.131375
obj = -2.677614, rho = -0.177173
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 271
nu = 0.109549
obj = -2.932315, rho = -0.170800
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*..*.*
optimization finished, #iter = 430
nu = 0.094829
obj = -3.228279, rho = -0.173321
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*
optimization finished, #iter = 378
nu = 0.080223
obj = -3.567026, rho = -0.220145
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
....*
optimization finished, #iter = 460
nu = 0.068515
obj = -3.986497, rho = -0.254174
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 345
nu = 0.059955
obj = -4.491302, rho = -0.257723
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 255
nu = 0.056670
obj = -4.989096, rho = -0.239631
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*
optimization finished, #iter = 373
nu = 0.051682
obj = -5.365760, rho = -0.285070
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
......*...*
optimization finished, #iter = 993
nu = 0.045348
obj = -5.584652, rho = -0.351764
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*..*
optimization finished, #iter = 529
nu = 0.038183
obj = -5.682394, rho = -0.466340
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*..*
optimization finished, #iter = 529
nu = 0.029964
obj = -5.682394, rho = -0.466340
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*..*
optimization finished, #iter = 529
nu = 0.023515
obj = -5.682394, rho = -0.466340
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*..*
optimization finished, #iter = 529
nu = 0.018453
obj = -5.682394, rho = -0.466340
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*..*
optimization finished, #iter = 529
nu = 0.014482
obj = -5.682394, rho = -0.466340
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*..*
optimization finished, #iter = 529
nu = 0.011365
obj = -5.682394, rho = -0.466340
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.194773
obj = -1.307570, rho = -0.300023
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.180788
obj = -1.465473, rho = -0.416844
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.163484
obj = -1.607930, rho = -0.540228
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.141186
obj = -1.746950, rho = -0.527930
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.121006
obj = -1.888524, rho = -0.572210
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.104818
obj = -2.024549, rho = -0.569011
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.090321
obj = -2.146171, rho = -0.596743
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*..*
optimization finished, #iter = 449
nu = 0.075557
obj = -2.240451, rho = -0.685346
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*
optimization finished, #iter = 425
nu = 0.062652
obj = -2.314862, rho = -0.729307
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.051901
obj = -2.370978, rho = -0.772618
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.042156
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.033082
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.025962
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.020374
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.015989
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.012547
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.009847
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.007727
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.006064
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.004759
obj = -2.379443, rho = -0.805414
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.207519
obj = -1.335538, rho = 0.072515
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.186752
obj = -1.476775, rho = -0.004267
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.160300
obj = -1.622340, rho = -0.019065
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.137893
obj = -1.791734, rho = 0.043588
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.117336
obj = -1.983185, rho = 0.066554
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.104434
obj = -2.206914, rho = 0.111786
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.092299
obj = -2.417836, rho = 0.144021
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.077946
obj = -2.647609, rho = 0.150062
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.067338
obj = -2.922227, rho = 0.127842
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.057928
obj = -3.216176, rho = 0.087669
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.054167
obj = -3.490285, rho = -0.045292
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.049894
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.039155
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.030727
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.024113
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.018923
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.014850
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.011654
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.009145
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.007177
obj = -3.589146, rho = -0.213807
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.191079
obj = -1.245560, rho = -0.198563
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.168929
obj = -1.393213, rho = -0.302385
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.146788
obj = -1.558523, rho = -0.319941
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.130007
obj = -1.751253, rho = -0.334912
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.115025
obj = -1.957320, rho = -0.288714
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.108386
obj = -2.144655, rho = -0.187312
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 225
nu = 0.091893
obj = -2.294123, rho = -0.187909
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.076451
obj = -2.466057, rho = -0.282397
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.068513
obj = -2.606659, rho = -0.254493
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.059165
obj = -2.681879, rho = -0.279746
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.047634
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.037381
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.029335
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.023021
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.018066
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.014177
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.011126
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.008731
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.006852
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*....*
optimization finished, #iter = 645
nu = 0.005377
obj = -2.688727, rho = -0.334395
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.166215
obj = -1.163139, rho = -0.215178
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.149299
obj = -1.337200, rho = -0.250490
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.138110
obj = -1.530368, rho = -0.341846
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 309
nu = 0.126574
obj = -1.729722, rho = -0.442154
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.112506
obj = -1.947217, rho = -0.422442
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.099144
obj = -2.185709, rho = -0.428443
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.091601
obj = -2.426897, rho = -0.457803
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.080720
obj = -2.634548, rho = -0.438827
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.073026
obj = -2.826894, rho = -0.416927
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.062897
obj = -2.902346, rho = -0.444817
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.051544
obj = -2.942502, rho = -0.326364
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.041015
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.032187
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.025259
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.019822
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.015556
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.012208
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.009580
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.007518
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*..*
optimization finished, #iter = 698
nu = 0.005900
obj = -2.950461, rho = -0.301369
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.161561
obj = -0.986286, rho = -0.126367
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.137563
obj = -1.076321, rho = -0.144623
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.121571
obj = -1.175386, rho = -0.105322
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.103195
obj = -1.269569, rho = -0.087144
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.087726
obj = -1.368592, rho = -0.096076
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.073811
obj = -1.477829, rho = -0.128120
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.066616
obj = -1.575423, rho = -0.052239
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.054926
obj = -1.645826, rho = -0.147296
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.045690
obj = -1.713288, rho = -0.362688
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.038019
obj = -1.763860, rho = -0.489167
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.031079
obj = -1.792355, rho = -0.470779
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.024948
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.019578
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.015364
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.012057
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.009462
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.007425
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.005827
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.004573
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.003589
obj = -1.794305, rho = -0.480743
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.147228
obj = -0.856292, rho = -0.356050
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.123908
obj = -0.912789, rho = -0.348238
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.104587
obj = -0.974476, rho = -0.321407
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.086975
obj = -1.040742, rho = -0.308277
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.074591
obj = -1.107024, rho = -0.381683
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 285
nu = 0.062463
obj = -1.165801, rho = -0.334847
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*............*
optimization finished, #iter = 1204
nu = 0.051069
obj = -1.223788, rho = -0.359506
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.042475
obj = -1.292433, rho = -0.394104
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.037140
obj = -1.336989, rho = -0.452875
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.030374
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.023836
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.018706
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.014680
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.011520
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.009040
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.007095
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.005567
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.004369
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.003429
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.002691
obj = -1.345409, rho = -0.425509
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.190048
obj = -1.288477, rho = -0.085866
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.173839
obj = -1.456828, rho = -0.107094
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 203
nu = 0.153510
obj = -1.631474, rho = -0.120458
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*
optimization finished, #iter = 344
nu = 0.131260
obj = -1.841658, rho = -0.129151
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.116171
obj = -2.098659, rho = -0.114927
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*....*
optimization finished, #iter = 571
nu = 0.102378
obj = -2.394181, rho = -0.092027
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.091046
obj = -2.754537, rho = -0.138335
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.081535
obj = -3.184606, rho = -0.155413
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.075639
obj = -3.669144, rho = -0.080161
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.069550
obj = -4.188517, rho = -0.059769
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.062562
obj = -4.742743, rho = 0.038742
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.058216
obj = -5.312645, rho = 0.137833
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 274
nu = 0.051920
obj = -5.819678, rho = 0.188368
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.043292
obj = -6.399958, rho = 0.166964
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 309
nu = 0.036522
obj = -7.134192, rho = 0.139888
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.031783
obj = -8.054805, rho = 0.074215
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.030320
obj = -8.966570, rho = 0.101007
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.028915
obj = -9.551514, rho = 0.275146
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.024500
obj = -9.616108, rho = 0.340377
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
..*
optimization finished, #iter = 243
nu = 0.019227
obj = -9.616108, rho = 0.340377
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 93.8% (938/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.141852
obj = -0.870762, rho = -0.184323
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.124128
obj = -0.950910, rho = -0.148700
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 169
nu = 0.107123
obj = -1.023211, rho = -0.119285
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.090942
obj = -1.098913, rho = -0.104812
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.077685
obj = -1.174442, rho = -0.119725
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.065659
obj = -1.243804, rho = -0.157453
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.056167
obj = -1.306402, rho = -0.186374
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.047438
obj = -1.339076, rho = -0.147674
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.038838
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.030479
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.023918
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.018770
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.014730
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.011560
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.009072
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.007119
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.005587
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.004384
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.003441
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.002700
obj = -1.349808, rho = -0.105248
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.187092
obj = -1.251778, rho = -0.108645
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 152
nu = 0.163463
obj = -1.418226, rho = -0.101612
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.142363
obj = -1.621378, rho = -0.100775
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.130505
obj = -1.857364, rho = -0.133374
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.123343
obj = -2.098444, rho = -0.111968
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.110597
obj = -2.324029, rho = -0.207886
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.093086
obj = -2.577955, rho = -0.216451
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.080244
obj = -2.892723, rho = -0.193482
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 210
nu = 0.069681
obj = -3.271024, rho = -0.206370
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.063583
obj = -3.694191, rho = -0.255487
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*..*
optimization finished, #iter = 468
nu = 0.057249
obj = -4.107344, rho = -0.309381
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*...*
optimization finished, #iter = 500
nu = 0.052566
obj = -4.522704, rho = -0.410637
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.046855
obj = -4.798405, rho = -0.505406
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.038917
obj = -5.059474, rho = -0.512196
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.034864
obj = -5.206533, rho = -0.523086
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.027452
obj = -5.206635, rho = -0.522594
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.021543
obj = -5.206635, rho = -0.522594
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.016906
obj = -5.206635, rho = -0.522594
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.013267
obj = -5.206635, rho = -0.522594
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.010412
obj = -5.206635, rho = -0.522594
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.213128
obj = -1.372455, rho = -0.078714
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.181497
obj = -1.537311, rho = -0.075637
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.159227
obj = -1.738441, rho = -0.083419
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.146997
obj = -1.947987, rho = 0.020370
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.126484
obj = -2.178518, rho = 0.025646
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.112620
obj = -2.428864, rho = 0.087043
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.099291
obj = -2.694429, rho = 0.102108
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.087665
obj = -2.972152, rho = 0.121113
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.074632
obj = -3.274518, rho = 0.049755
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.063575
obj = -3.640537, rho = 0.012910
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.055528
obj = -4.064832, rho = 0.110019
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.050496
obj = -4.510027, rho = 0.218887
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
...*
optimization finished, #iter = 390
nu = 0.044319
obj = -4.936676, rho = 0.089757
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
...*...*
optimization finished, #iter = 679
nu = 0.038609
obj = -5.355719, rho = -0.010010
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*......*
optimization finished, #iter = 828
nu = 0.034118
obj = -5.726028, rho = -0.127838
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
....*...............*
optimization finished, #iter = 1912
nu = 0.029068
obj = -5.976271, rho = -0.194124
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
....*.*
optimization finished, #iter = 574
nu = 0.025157
obj = -6.080588, rho = -0.297027
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
....*.*
optimization finished, #iter = 574
nu = 0.019742
obj = -6.080588, rho = -0.297027
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
....*.*
optimization finished, #iter = 574
nu = 0.015493
obj = -6.080588, rho = -0.297027
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
....*.*
optimization finished, #iter = 574
nu = 0.012158
obj = -6.080588, rho = -0.297027
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.205646
obj = -1.292887, rho = 0.053565
nSV = 23, nBSV = 19
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.181097
obj = -1.421300, rho = -0.040715
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.158970
obj = -1.542400, rho = -0.019891
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.134699
obj = -1.672872, rho = 0.042051
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.113174
obj = -1.816526, rho = 0.066126
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.097877
obj = -1.984528, rho = 0.075633
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.087727
obj = -2.131694, rho = 0.153758
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.077228
obj = -2.215594, rho = 0.217039
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
..*.*
optimization finished, #iter = 355
nu = 0.063146
obj = -2.257552, rho = 0.236884
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
..*..*
optimization finished, #iter = 486
nu = 0.051429
obj = -2.279879, rho = 0.211487
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.040391
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.031698
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.024875
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.019521
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.015319
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.012022
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.009434
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.007404
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.005810
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*...*
optimization finished, #iter = 522
nu = 0.004560
obj = -2.279891, rho = 0.210944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.182738
obj = -1.207963, rho = 0.063561
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.158948
obj = -1.365596, rho = -0.002421
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.143848
obj = -1.549144, rho = -0.032480
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.130104
obj = -1.736435, rho = -0.017447
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*..*
optimization finished, #iter = 233
nu = 0.117866
obj = -1.918539, rho = 0.025467
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.099697
obj = -2.109960, rho = 0.024031
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.084797
obj = -2.341820, rho = 0.011620
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.074746
obj = -2.595588, rho = -0.038223
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.068062
obj = -2.847592, rho = 0.002933
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.060364
obj = -3.071030, rho = 0.003608
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.054218
obj = -3.195589, rho = 0.054637
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.043524
obj = -3.254826, rho = 0.066491
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 298
nu = 0.035159
obj = -3.318704, rho = 0.048100
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.028466
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.022339
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.017531
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.013757
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.010796
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.008472
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.006649
obj = -3.324993, rho = 0.053285
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.190979
obj = -1.279977, rho = 0.047592
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.170492
obj = -1.442461, rho = 0.121770
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.152143
obj = -1.623934, rho = 0.254294
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.132586
obj = -1.824683, rho = 0.298439
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*............*
optimization finished, #iter = 1348
nu = 0.116308
obj = -2.062203, rho = 0.300143
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.....*.....*
optimization finished, #iter = 1004
nu = 0.101803
obj = -2.340182, rho = 0.294725
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.089391
obj = -2.680674, rho = 0.339040
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.078972
obj = -3.085722, rho = 0.370779
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.072952
obj = -3.566621, rho = 0.328045
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.068055
obj = -4.073507, rho = 0.322061
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.063779
obj = -4.573807, rho = 0.380656
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*...*
optimization finished, #iter = 583
nu = 0.056154
obj = -5.034069, rho = 0.399932
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 382
nu = 0.047812
obj = -5.560090, rho = 0.389370
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.....*
optimization finished, #iter = 541
nu = 0.040538
obj = -6.189986, rho = 0.386565
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.034566
obj = -6.985452, rho = 0.393188
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.029998
obj = -7.986415, rho = 0.426211
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.027474
obj = -9.199071, rho = 0.542817
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.026636
obj = -10.359203, rho = 0.798683
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.025832
obj = -11.174140, rho = 0.985516
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.022614
obj = -11.309655, rho = 1.098376
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.157613
obj = -1.030437, rho = 0.013231
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.139126
obj = -1.152899, rho = 0.080183
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.121451
obj = -1.295053, rho = 0.071719
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.107990
obj = -1.443918, rho = 0.041209
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.092591
obj = -1.617998, rho = 0.050458
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.086698
obj = -1.812691, rho = 0.080439
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.077554
obj = -1.970854, rho = 0.025790
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.066876
obj = -2.126189, rho = 0.056233
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.058235
obj = -2.257330, rho = 0.124605
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.050894
obj = -2.330579, rho = 0.185103
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.041464
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.032539
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.025535
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.020039
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.015726
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.012341
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.009685
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.007600
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.005964
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.004681
obj = -2.340093, rho = 0.180949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.265896
obj = -1.858054, rho = 0.348981
nSV = 29, nBSV = 22
Total nSV = 29
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.238940
obj = -2.137121, rho = 0.373155
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.222324
obj = -2.438718, rho = 0.346080
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.195805
obj = -2.773556, rho = 0.416995
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.170197
obj = -3.177998, rho = 0.446785
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.151298
obj = -3.685576, rho = 0.407773
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.136719
obj = -4.290610, rho = 0.337033
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 284
nu = 0.124529
obj = -5.004673, rho = 0.300001
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*..*
optimization finished, #iter = 285
nu = 0.115154
obj = -5.828640, rho = 0.251511
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.104564
obj = -6.807813, rho = 0.138383
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.095604
obj = -7.931233, rho = 0.013874
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.088583
obj = -9.244972, rho = -0.006153
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.....*
optimization finished, #iter = 580
nu = 0.081861
obj = -10.712890, rho = 0.017495
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.076405
obj = -12.327626, rho = 0.134934
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*.*
optimization finished, #iter = 451
nu = 0.072584
obj = -13.917517, rho = 0.361306
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 379
nu = 0.068058
obj = -15.286964, rho = 0.558594
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*.*
optimization finished, #iter = 503
nu = 0.056769
obj = -16.547065, rho = 0.336755
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.....*..*
optimization finished, #iter = 735
nu = 0.047535
obj = -18.060565, rho = 0.332215
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.......*.*
optimization finished, #iter = 815
nu = 0.042007
obj = -19.747230, rho = 0.879121
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
...*.*
optimization finished, #iter = 498
nu = 0.035876
obj = -21.305092, rho = 1.140023
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.206496
obj = -1.418537, rho = -0.097582
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.187123
obj = -1.610201, rho = -0.040995
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.173174
obj = -1.819159, rho = 0.111910
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.155201
obj = -2.015981, rho = 0.222918
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.135126
obj = -2.230310, rho = 0.266953
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.**.*
optimization finished, #iter = 218
nu = 0.122143
obj = -2.435942, rho = 0.425210
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*..*
optimization finished, #iter = 545
nu = 0.109587
obj = -2.593240, rho = 0.458424
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.089837
obj = -2.721591, rho = 0.448631
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*
optimization finished, #iter = 362
nu = 0.074937
obj = -2.855750, rho = 0.401120
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 360
nu = 0.062834
obj = -2.939556, rho = 0.332254
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 304
nu = 0.050066
obj = -3.024865, rho = 0.332263
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.041273
obj = -3.114591, rho = 0.249701
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.034339
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.026948
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.021147
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.016596
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.013024
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.010220
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.008021
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.006294
obj = -3.146816, rho = 0.145676
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.144639
obj = -0.930977, rho = -0.106817
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.127240
obj = -1.036693, rho = -0.129136
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.109688
obj = -1.154433, rho = -0.127929
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.096665
obj = -1.287326, rho = -0.048861
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.082516
obj = -1.441400, rho = -0.025186
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.072005
obj = -1.629781, rho = -0.101271
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.064331
obj = -1.848723, rho = -0.118281
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.058641
obj = -2.075859, rho = -0.043141
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.052563
obj = -2.320680, rho = -0.116889
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.049478
obj = -2.528683, rho = -0.205478
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.043147
obj = -2.659456, rho = -0.117514
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.037926
obj = -2.732419, rho = 0.058829
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.029811
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.023394
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.018359
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.014407
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.011306
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.008873
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.006963
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.005464
obj = -2.732431, rho = 0.061598
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.185158
obj = -1.208784, rho = -0.201174
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.162744
obj = -1.351634, rho = -0.230261
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.141571
obj = -1.516202, rho = -0.264220
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.128005
obj = -1.684907, rho = -0.206602
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.110013
obj = -1.881045, rho = -0.225339
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*..*
optimization finished, #iter = 206
nu = 0.097841
obj = -2.085160, rho = -0.274651
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*..*
optimization finished, #iter = 256
nu = 0.084998
obj = -2.315500, rho = -0.384704
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.075060
obj = -2.562959, rho = -0.438756
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 263
nu = 0.065689
obj = -2.816972, rho = -0.458526
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.056533
obj = -3.092039, rho = -0.517794
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*....*
optimization finished, #iter = 649
nu = 0.052063
obj = -3.332152, rho = -0.538881
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*....*
optimization finished, #iter = 632
nu = 0.047271
obj = -3.445211, rho = -0.529173
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.037609
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.029514
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.023162
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.018176
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.014264
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.011194
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.008784
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.....*
optimization finished, #iter = 617
nu = 0.006894
obj = -3.446963, rho = -0.528889
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.202299
obj = -1.368657, rho = -0.367157
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.184226
obj = -1.542154, rho = -0.287227
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.158523
obj = -1.740773, rho = -0.289400
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.141967
obj = -1.974085, rho = -0.333608
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.124304
obj = -2.242747, rho = -0.429784
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.110533
obj = -2.561379, rho = -0.535067
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.102951
obj = -2.910738, rho = -0.660372
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.090315
obj = -3.280534, rho = -0.648941
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*.*
optimization finished, #iter = 351
nu = 0.080358
obj = -3.700041, rho = -0.588294
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 317
nu = 0.073582
obj = -4.143386, rho = -0.537542
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.063734
obj = -4.592292, rho = -0.434366
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 324
nu = 0.054921
obj = -5.113157, rho = -0.469432
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.048089
obj = -5.716565, rho = -0.510025
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.043354
obj = -6.359732, rho = -0.694257
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.037920
obj = -7.021942, rho = -0.956812
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.034966
obj = -7.611457, rho = -1.483408
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*..*
optimization finished, #iter = 444
nu = 0.032436
obj = -7.878790, rho = -2.134473
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*...*
optimization finished, #iter = 466
nu = 0.025584
obj = -7.879518, rho = -2.148372
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*...*
optimization finished, #iter = 466
nu = 0.020077
obj = -7.879518, rho = -2.148372
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*...*
optimization finished, #iter = 466
nu = 0.015756
obj = -7.879518, rho = -2.148372
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.170951
obj = -1.144064, rho = -0.201637
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.157965
obj = -1.284088, rho = -0.157845
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.139579
obj = -1.418153, rho = -0.259387
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.118861
obj = -1.566526, rho = -0.293171
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.102423
obj = -1.740821, rho = -0.300759
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.090017
obj = -1.947959, rho = -0.368392
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.079183
obj = -2.172712, rho = -0.385052
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.073538
obj = -2.382769, rho = -0.435996
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*...................*
optimization finished, #iter = 2031
nu = 0.061343
obj = -2.578041, rho = -0.447246
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.053442
obj = -2.792799, rho = -0.370497
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.047330
obj = -2.975171, rho = -0.233760
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.042187
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.033107
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.025981
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.020389
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.016000
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.012556
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.009854
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.007733
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.006068
obj = -3.034315, rho = 0.029549
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.177757
obj = -1.124340, rho = -0.029778
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.152685
obj = -1.246996, rho = -0.000007
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.136909
obj = -1.378077, rho = -0.000905
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.119817
obj = -1.507348, rho = -0.036055
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.101227
obj = -1.646574, rho = -0.097199
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.085232
obj = -1.812919, rho = -0.129056
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.073611
obj = -2.014099, rho = -0.232270
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.062600
obj = -2.250283, rho = -0.282380
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.054265
obj = -2.541886, rho = -0.284834
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.049820
obj = -2.873165, rho = -0.134933
nSV = 8, nBSV = 3
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.047639
obj = -3.156609, rho = 0.167778
nSV = 8, nBSV = 3
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.041941
obj = -3.343567, rho = 0.297445
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.037315
obj = -3.437442, rho = 0.613015
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.029435
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.023100
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.018128
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.014226
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.011164
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.008761
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.006875
obj = -3.437614, rho = 0.627043
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.187180
obj = -1.308897, rho = -0.111540
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.165030
obj = -1.510017, rho = -0.092872
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.146777
obj = -1.757207, rho = -0.081217
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.133008
obj = -2.063493, rho = -0.021759
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.122587
obj = -2.426492, rho = 0.088450
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.115085
obj = -2.846318, rho = 0.165555
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.111134
obj = -3.292202, rho = 0.247798
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.105717
obj = -3.711939, rho = 0.313856
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.092466
obj = -4.140352, rho = 0.268185
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.081459
obj = -4.616607, rho = 0.292334
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.073647
obj = -5.098782, rho = 0.496566
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.064759
obj = -5.533499, rho = 0.601529
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.055323
obj = -5.953144, rho = 0.625522
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.049914
obj = -6.317886, rho = 0.513544
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.042972
obj = -6.396459, rho = 0.360584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.033723
obj = -6.396459, rho = 0.360584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.026464
obj = -6.396459, rho = 0.360584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.020768
obj = -6.396459, rho = 0.360584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.016298
obj = -6.396459, rho = 0.360584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.012790
obj = -6.396459, rho = 0.360584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.259025
obj = -1.923726, rho = -0.240595
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.240000
obj = -2.257793, rho = -0.175048
nSV = 27, nBSV = 22
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.217226
obj = -2.640954, rho = -0.252554
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.194701
obj = -3.120815, rho = -0.261417
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.180192
obj = -3.707247, rho = -0.338152
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.165853
obj = -4.423113, rho = -0.319247
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.153853
obj = -5.298374, rho = -0.210729
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 111
nu = 0.143493
obj = -6.384403, rho = -0.296067
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.136521
obj = -7.697058, rho = -0.439926
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*...*
optimization finished, #iter = 688
nu = 0.128668
obj = -9.271240, rho = -0.549969
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.123490
obj = -11.160634, rho = -0.734224
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 220
nu = 0.117050
obj = -13.372124, rho = -0.845589
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.111752
obj = -15.965780, rho = -0.902239
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.108666
obj = -18.871582, rho = -0.954221
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.098810
obj = -22.158786, rho = -1.050094
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*
optimization finished, #iter = 367
nu = 0.094420
obj = -25.992840, rho = -1.011614
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.......*..*
optimization finished, #iter = 940
nu = 0.086835
obj = -30.010003, rho = -0.974993
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
......*..*
optimization finished, #iter = 828
nu = 0.076765
obj = -34.911326, rho = -1.023178
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
........*.*
optimization finished, #iter = 928
nu = 0.074413
obj = -40.483761, rho = -0.966420
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
...*........*
optimization finished, #iter = 1193
nu = 0.066079
obj = -46.246726, rho = -1.018957
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.201540
obj = -1.333375, rho = -0.253093
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.177109
obj = -1.498468, rho = -0.312877
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.157592
obj = -1.689499, rho = -0.293344
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.143648
obj = -1.887711, rho = -0.265597
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.125171
obj = -2.100820, rho = -0.260324
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 234
nu = 0.110236
obj = -2.320305, rho = -0.259612
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.094701
obj = -2.569604, rho = -0.279543
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 219
nu = 0.084531
obj = -2.819245, rho = -0.288224
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 358
nu = 0.071450
obj = -3.094042, rho = -0.265629
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.061576
obj = -3.422083, rho = -0.272295
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.056262
obj = -3.730097, rho = -0.254693
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.049427
obj = -3.939988, rho = -0.225395
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.040527
obj = -4.136284, rho = -0.198393
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*
optimization finished, #iter = 293
nu = 0.033959
obj = -4.335952, rho = -0.244631
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.029680
obj = -4.417737, rho = -0.206811
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.023292
obj = -4.417737, rho = -0.206811
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.018279
obj = -4.417737, rho = -0.206811
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.014344
obj = -4.417737, rho = -0.206811
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.011257
obj = -4.417737, rho = -0.206811
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.008834
obj = -4.417737, rho = -0.206811
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.189812
obj = -1.291869, rho = -0.047283
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.170018
obj = -1.466820, rho = -0.121602
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.151439
obj = -1.661388, rho = -0.119331
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.134873
obj = -1.886755, rho = -0.093918
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.123155
obj = -2.125312, rho = 0.081194
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.110976
obj = -2.371005, rho = 0.210797
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.094813
obj = -2.635635, rho = 0.213738
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 261
nu = 0.081630
obj = -2.962974, rho = 0.204432
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.071665
obj = -3.355146, rho = 0.196498
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.065814
obj = -3.781781, rho = 0.349226
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.058719
obj = -4.195961, rho = 0.532937
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*.*
optimization finished, #iter = 375
nu = 0.052062
obj = -4.601216, rho = 0.596483
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.045139
obj = -5.044887, rho = 0.611493
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
..*.*
optimization finished, #iter = 308
nu = 0.040536
obj = -5.440196, rho = 0.681693
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
..*
optimization finished, #iter = 299
nu = 0.036738
obj = -5.681050, rho = 0.732456
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94% (940/1000) (classification)
.....*.....*
optimization finished, #iter = 1010
nu = 0.030043
obj = -5.696920, rho = 0.745301
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
.....*.....*
optimization finished, #iter = 1010
nu = 0.023576
obj = -5.696920, rho = 0.745301
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
.....*.....*
optimization finished, #iter = 1010
nu = 0.018502
obj = -5.696920, rho = 0.745301
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
.....*.....*
optimization finished, #iter = 1010
nu = 0.014519
obj = -5.696920, rho = 0.745301
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
.....*.....*
optimization finished, #iter = 1010
nu = 0.011394
obj = -5.696920, rho = 0.745301
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.7% (937/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.179028
obj = -1.133844, rho = -0.190335
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.158849
obj = -1.254743, rho = -0.203205
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.139594
obj = -1.364865, rho = -0.237341
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.119381
obj = -1.476034, rho = -0.288844
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.099912
obj = -1.604888, rho = -0.277899
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.089944
obj = -1.726350, rho = -0.186606
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.076475
obj = -1.822605, rho = -0.155621
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.064088
obj = -1.901405, rho = -0.274333
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.052768
obj = -1.981678, rho = -0.140787
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*...*
optimization finished, #iter = 406
nu = 0.043338
obj = -2.044428, rho = -0.051967
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 286
nu = 0.036629
obj = -2.090337, rho = 0.059436
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.029071
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.022814
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.017903
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.014050
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.011026
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.008652
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.006790
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.005329
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 404
nu = 0.004182
obj = -2.090832, rho = 0.076016
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.196818
obj = -1.243295, rho = -0.156813
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*..*
optimization finished, #iter = 382
nu = 0.174127
obj = -1.373289, rho = -0.154057
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.153748
obj = -1.496393, rho = -0.188408
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.135007
obj = -1.616320, rho = -0.175324
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*..*
optimization finished, #iter = 385
nu = 0.114441
obj = -1.712442, rho = -0.110124
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*..*
optimization finished, #iter = 313
nu = 0.094573
obj = -1.815261, rho = -0.122221
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.079755
obj = -1.919241, rho = -0.139888
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*.*.*
optimization finished, #iter = 356
nu = 0.066097
obj = -2.028849, rho = -0.107411
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
...*.*
optimization finished, #iter = 481
nu = 0.054487
obj = -2.137150, rho = -0.091994
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*.*
optimization finished, #iter = 386
nu = 0.044268
obj = -2.270423, rho = -0.093317
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.037289
obj = -2.436337, rho = -0.069875
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*..*...*
optimization finished, #iter = 581
nu = 0.034274
obj = -2.542085, rho = 0.058308
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.027779
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.021800
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.017108
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.013426
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.010536
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.008268
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.006488
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*....*
optimization finished, #iter = 677
nu = 0.005092
obj = -2.546089, rho = 0.084205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.167673
obj = -1.096399, rho = -0.062008
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.146857
obj = -1.233976, rho = -0.063880
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.132053
obj = -1.380754, rho = -0.076596
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.115768
obj = -1.539814, rho = -0.138593
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.104730
obj = -1.700696, rho = -0.148330
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.089381
obj = -1.859823, rho = -0.129682
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.078309
obj = -2.028354, rho = -0.200264
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.067432
obj = -2.210406, rho = -0.217823
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.056885
obj = -2.398802, rho = -0.221919
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.048456
obj = -2.615266, rho = -0.210478
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.044129
obj = -2.819171, rho = -0.020819
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 399
nu = 0.039635
obj = -2.910633, rho = 0.169464
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.031788
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.024946
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.019577
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.015363
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.012056
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.009461
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.007425
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.005827
obj = -2.913683, rho = 0.214012
nSV = 12, nBSV = 0
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.200008
obj = -1.311444, rho = -0.007047
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.176371
obj = -1.469096, rho = -0.001724
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.152438
obj = -1.654257, rho = -0.007924
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.140704
obj = -1.857413, rho = -0.027897
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.125174
obj = -2.049799, rho = 0.075005
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.111202
obj = -2.234269, rho = 0.264927
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.094094
obj = -2.417568, rho = 0.324036
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.079702
obj = -2.631691, rho = 0.352316
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.069600
obj = -2.840794, rho = 0.274121
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.060485
obj = -3.029322, rho = 0.204521
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*....*
optimization finished, #iter = 538
nu = 0.050196
obj = -3.195304, rho = 0.253366
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.042650
obj = -3.363243, rho = 0.279933
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.035688
obj = -3.491874, rho = 0.337931
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*....*
optimization finished, #iter = 729
nu = 0.029981
obj = -3.556932, rho = 0.342575
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.023936
obj = -3.562630, rho = 0.323115
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.018784
obj = -3.562630, rho = 0.323115
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.014741
obj = -3.562630, rho = 0.323115
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.011568
obj = -3.562630, rho = 0.323115
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.009078
obj = -3.562630, rho = 0.323115
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.007124
obj = -3.562630, rho = 0.323115
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.194188
obj = -1.256703, rho = 0.130757
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.165472
obj = -1.410638, rho = 0.141211
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.150042
obj = -1.582703, rho = 0.069452
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.128229
obj = -1.779989, rho = 0.035343
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.110716
obj = -2.025153, rho = 0.044199
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.097974
obj = -2.325446, rho = 0.041188
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.086651
obj = -2.695904, rho = 0.040354
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.080435
obj = -3.130320, rho = 0.039306
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.078237
obj = -3.561752, rho = 0.261348
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.073996
obj = -3.936083, rho = 0.560746
nSV = 10, nBSV = 5
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.066475
obj = -4.174136, rho = 0.668398
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.054709
obj = -4.387322, rho = 0.608905
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.048092
obj = -4.577820, rho = 0.622514
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.039277
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.030823
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.024189
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.018982
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.014896
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.011690
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.009174
obj = -4.586551, rho = 0.648518
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.184509
obj = -1.133065, rho = -0.179625
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.156793
obj = -1.241560, rho = -0.188246
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.134946
obj = -1.366567, rho = -0.225985
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 292
nu = 0.115801
obj = -1.507411, rho = -0.214040
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*...*
optimization finished, #iter = 322
nu = 0.099005
obj = -1.666016, rho = -0.222984
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.089246
obj = -1.844918, rho = -0.284142
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.080100
obj = -1.996424, rho = -0.385811
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.069857
obj = -2.107733, rho = -0.474065
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.057368
obj = -2.213616, rho = -0.531268
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.048785
obj = -2.313488, rho = -0.581383
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.039743
obj = -2.381016, rho = -0.614179
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.032596
obj = -2.438593, rho = -0.705531
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.026795
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.021028
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.016502
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.012950
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.010162
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.007975
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.006259
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.004911
obj = -2.455937, rho = -0.834601
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.199055
obj = -1.289171, rho = -0.173920
nSV = 26, nBSV = 15
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.172795
obj = -1.446635, rho = -0.188986
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.156053
obj = -1.614597, rho = -0.277435
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 261
nu = 0.137983
obj = -1.779019, rho = -0.360483
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.119910
obj = -1.961377, rho = -0.433046
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.105456
obj = -2.133572, rho = -0.534616
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.090942
obj = -2.311468, rho = -0.567242
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.079576
obj = -2.474900, rho = -0.472310
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.068366
obj = -2.605195, rho = -0.601808
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.055987
obj = -2.711882, rho = -0.637456
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*....*
optimization finished, #iter = 638
nu = 0.046811
obj = -2.809636, rho = -0.729559
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
...*.*
optimization finished, #iter = 424
nu = 0.038010
obj = -2.895614, rho = -0.745039
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.031874
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.025014
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.019630
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.015405
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.012089
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.009487
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.007445
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.005843
obj = -2.921663, rho = -0.865738
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.185665
obj = -1.294277, rho = -0.124071
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.166709
obj = -1.489109, rho = -0.121377
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.147307
obj = -1.717388, rho = -0.102140
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.139842
obj = -1.978062, rho = 0.048685
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.123123
obj = -2.255682, rho = 0.013128
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.111392
obj = -2.585527, rho = -0.242179
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.097757
obj = -2.965699, rho = -0.317175
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.089371
obj = -3.430851, rho = -0.576309
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.078802
obj = -3.963736, rho = -0.660991
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.071066
obj = -4.616644, rho = -0.742389
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.065306
obj = -5.388426, rho = -0.847764
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.059898
obj = -6.284779, rho = -0.924476
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.055303
obj = -7.289416, rho = -0.998818
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.050361
obj = -8.452774, rho = -0.965192
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.045398
obj = -9.839308, rho = -0.936558
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.042448
obj = -11.407981, rho = -0.878583
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.040133
obj = -13.050947, rho = -0.804972
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.038316
obj = -14.566547, rho = -0.711034
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.036890
obj = -15.559308, rho = -0.591063
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 310
nu = 0.031317
obj = -15.662947, rho = -0.538236
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.204214
obj = -1.475932, rho = -0.086814
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.185667
obj = -1.719884, rho = -0.175495
nSV = 21, nBSV = 17
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.169082
obj = -1.999683, rho = -0.224224
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.152648
obj = -2.333849, rho = -0.312941
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.139297
obj = -2.731271, rho = -0.370536
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.126481
obj = -3.214957, rho = -0.379801
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.120046
obj = -3.775758, rho = -0.284526
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.113422
obj = -4.385935, rho = -0.075149
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.102054
obj = -5.060978, rho = 0.058655
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.092339
obj = -5.859695, rho = 0.149142
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 238
nu = 0.084986
obj = -6.760269, rho = 0.207962
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.075820
obj = -7.821896, rho = 0.191518
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.070961
obj = -9.011351, rho = 0.154745
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*.*
optimization finished, #iter = 319
nu = 0.062790
obj = -10.302789, rho = 0.142527
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.054750
obj = -11.931262, rho = 0.143140
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.048440
obj = -14.006028, rho = 0.143620
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.044791
obj = -16.597927, rho = 0.038270
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.043124
obj = -19.537616, rho = -0.236183
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.040753
obj = -22.690250, rho = -0.493574
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.038880
obj = -25.932862, rho = -0.792891
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.217219
obj = -1.500098, rho = -0.130332
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.199538
obj = -1.706880, rho = -0.033961
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.176047
obj = -1.932872, rho = -0.018435
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.154854
obj = -2.199867, rho = -0.014190
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.141180
obj = -2.501188, rho = -0.031241
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.126485
obj = -2.818591, rho = -0.059786
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.114428
obj = -3.159973, rho = -0.224444
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*...*
optimization finished, #iter = 410
nu = 0.099545
obj = -3.518628, rho = -0.274400
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.......*
optimization finished, #iter = 855
nu = 0.085907
obj = -3.949155, rho = -0.275538
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*...........*
optimization finished, #iter = 1230
nu = 0.075182
obj = -4.462437, rho = -0.253609
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 331
nu = 0.065417
obj = -5.061671, rho = -0.218056
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.060438
obj = -5.744484, rho = 0.001775
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.053994
obj = -6.423684, rho = 0.146221
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.046676
obj = -7.186852, rho = 0.243234
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 262
nu = 0.040424
obj = -8.131474, rho = 0.166037
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.....*
optimization finished, #iter = 556
nu = 0.037077
obj = -9.178807, rho = -0.033888
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.032929
obj = -10.287032, rho = -0.084789
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.031365
obj = -11.253205, rho = -0.365365
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 359
nu = 0.029677
obj = -11.648572, rho = -0.723102
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*
optimization finished, #iter = 359
nu = 0.023289
obj = -11.648572, rho = -0.723102
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.205868
obj = -1.380302, rho = -0.208858
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.178661
obj = -1.570581, rho = -0.158382
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.158127
obj = -1.797574, rho = -0.115892
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.142958
obj = -2.061977, rho = -0.172247
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.130621
obj = -2.366678, rho = -0.092628
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.122421
obj = -2.676141, rho = -0.236231
nSV = 15, nBSV = 10
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.111512
obj = -2.954139, rho = -0.606016
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.099685
obj = -3.230118, rho = -0.776055
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.088884
obj = -3.435153, rho = -1.203720
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*.*
optimization finished, #iter = 271
nu = 0.073696
obj = -3.587784, rho = -1.325669
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.062304
obj = -3.740282, rho = -1.554980
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*.*
optimization finished, #iter = 327
nu = 0.049944
obj = -3.861257, rho = -1.571427
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 363
nu = 0.040768
obj = -3.990086, rho = -1.592109
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.034656
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.027197
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.021343
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.016749
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.013144
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.010315
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.008095
obj = -4.048293, rho = -1.720852
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.232775
obj = -1.677063, rho = -0.073471
nSV = 26, nBSV = 21
Total nSV = 26
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.220268
obj = -1.931773, rho = 0.085235
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.201313
obj = -2.193563, rho = 0.154833
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.178461
obj = -2.485855, rho = 0.142175
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*..*
optimization finished, #iter = 210
nu = 0.156785
obj = -2.828511, rho = 0.135020
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.138439
obj = -3.225063, rho = 0.129293
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.120554
obj = -3.718082, rho = 0.128277
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.113225
obj = -4.297947, rho = 0.130502
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.107518
obj = -4.858702, rho = 0.121891
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 232
nu = 0.091695
obj = -5.464105, rho = 0.118820
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 281
nu = 0.080412
obj = -6.212168, rho = 0.192863
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.072685
obj = -7.083359, rho = 0.292608
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 341
nu = 0.066952
obj = -7.956223, rho = 0.301964
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 364
nu = 0.061488
obj = -8.799629, rho = 0.386571
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 372
nu = 0.051857
obj = -9.661883, rho = 0.530668
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.044681
obj = -10.673742, rho = 0.716700
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.039206
obj = -11.771043, rho = 0.955080
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 246
nu = 0.033872
obj = -12.897537, rho = 1.029718
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.031700
obj = -13.909768, rho = 1.267614
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 365
nu = 0.028340
obj = -14.174056, rho = 1.486013
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.188959
obj = -1.193870, rho = -0.079415
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.165225
obj = -1.319649, rho = -0.101348
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.147766
obj = -1.440437, rho = -0.166914
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.124998
obj = -1.566541, rho = -0.123229
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.111296
obj = -1.695997, rho = -0.141152
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*...*
optimization finished, #iter = 305
nu = 0.098647
obj = -1.774865, rho = -0.162576
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.080489
obj = -1.832537, rho = -0.217610
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.067072
obj = -1.864443, rho = -0.268997
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.053612
obj = -1.872868, rho = -0.288098
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*
optimization finished, #iter = 481
nu = 0.042233
obj = -1.877982, rho = -0.290709
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.033271
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.026110
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.020490
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.016080
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.012619
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.009903
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.007771
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.006099
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.004786
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 658
nu = 0.003756
obj = -1.878367, rho = -0.295205
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 50
nu = 0.187982
obj = -1.225878, rho = -0.276289
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.165724
obj = -1.368166, rho = -0.284587
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.147769
obj = -1.523382, rho = -0.440394
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.132289
obj = -1.684869, rho = -0.569146
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 265
nu = 0.115478
obj = -1.826427, rho = -0.636730
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.099721
obj = -1.981204, rho = -0.718680
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*..*
optimization finished, #iter = 434
nu = 0.086563
obj = -2.113821, rho = -0.784544
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.070693
obj = -2.256085, rho = -0.785817
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.059550
obj = -2.425366, rho = -0.780518
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*.*
optimization finished, #iter = 596
nu = 0.049589
obj = -2.612817, rho = -0.779541
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.043249
obj = -2.806896, rho = -0.836879
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.037999
obj = -2.949285, rho = -1.021316
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.032607
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.025589
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.020081
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.015759
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.012367
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.009705
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.007616
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.005977
obj = -2.988258, rho = -1.130278
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.190436
obj = -1.217605, rho = -0.130671
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.165375
obj = -1.356325, rho = -0.110647
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.144811
obj = -1.509052, rho = -0.154883
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.124923
obj = -1.685648, rho = -0.196026
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.111884
obj = -1.884804, rho = -0.313604
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.104982
obj = -2.058636, rho = -0.444681
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.094104
obj = -2.162630, rho = -0.570495
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.079476
obj = -2.202242, rho = -0.599301
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*..*
optimization finished, #iter = 326
nu = 0.062765
obj = -2.218473, rho = -0.591029
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*...*
optimization finished, #iter = 628
nu = 0.050272
obj = -2.227020, rho = -0.570156
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.039454
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.030962
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.024298
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.019068
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.014964
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.011743
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.009215
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.007232
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.005675
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*...*
optimization finished, #iter = 688
nu = 0.004454
obj = -2.227021, rho = -0.570051
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.232455
obj = -1.726107, rho = -0.101931
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.220000
obj = -2.020321, rho = -0.171945
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.196055
obj = -2.353194, rho = -0.121493
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.179525
obj = -2.762594, rho = -0.139451
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.169578
obj = -3.226000, rho = -0.259009
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 96% (96/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.159803
obj = -3.711960, rho = -0.323006
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*..*
optimization finished, #iter = 221
nu = 0.142705
obj = -4.232789, rho = -0.368676
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*..*
optimization finished, #iter = 237
nu = 0.125668
obj = -4.866296, rho = -0.410660
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.117370
obj = -5.598272, rho = -0.329258
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...**.*
optimization finished, #iter = 347
nu = 0.112397
obj = -6.292315, rho = -0.505826
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.095659
obj = -6.978166, rho = -0.544633
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
...*
optimization finished, #iter = 326
nu = 0.085167
obj = -7.717598, rho = -0.653104
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*
optimization finished, #iter = 333
nu = 0.076788
obj = -8.465820, rho = -0.780726
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
....*..*
optimization finished, #iter = 655
nu = 0.065080
obj = -9.177351, rho = -0.764683
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.055491
obj = -10.026137, rho = -0.776620
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 94.7% (947/1000) (classification)
..*
optimization finished, #iter = 234
nu = 0.052412
obj = -10.674826, rho = -0.795177
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
..*.*
optimization finished, #iter = 372
nu = 0.044637
obj = -10.786275, rho = -0.846572
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 372
nu = 0.035029
obj = -10.786275, rho = -0.846572
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 372
nu = 0.027490
obj = -10.786275, rho = -0.846572
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 372
nu = 0.021573
obj = -10.786275, rho = -0.846572
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.158315
obj = -0.963541, rho = -0.054701
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.135594
obj = -1.049590, rho = -0.005342
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.117150
obj = -1.140994, rho = -0.039005
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.102740
obj = -1.228826, rho = 0.080660
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*........*
optimization finished, #iter = 808
nu = 0.086795
obj = -1.310431, rho = 0.131343
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*........*
optimization finished, #iter = 890
nu = 0.072509
obj = -1.388447, rho = 0.119435
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*.*
optimization finished, #iter = 299
nu = 0.059820
obj = -1.477513, rho = 0.085129
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 290
nu = 0.050328
obj = -1.575070, rho = 0.090123
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*..*
optimization finished, #iter = 345
nu = 0.042379
obj = -1.673794, rho = 0.049111
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 167
nu = 0.036665
obj = -1.758800, rho = -0.016189
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.030994
obj = -1.811840, rho = -0.018953
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.025281
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.019840
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.015570
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.012218
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.009588
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.007525
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.005905
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.004634
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.003637
obj = -1.818610, rho = -0.019004
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.205412
obj = -1.351526, rho = -0.270914
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.176811
obj = -1.525623, rho = -0.281517
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.158063
obj = -1.730829, rho = -0.317283
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.143324
obj = -1.958193, rho = -0.362686
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.130992
obj = -2.185939, rho = -0.378315
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*....*
optimization finished, #iter = 566
nu = 0.118014
obj = -2.391665, rho = -0.407751
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*.*
optimization finished, #iter = 317
nu = 0.100885
obj = -2.599763, rho = -0.445996
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.089360
obj = -2.797711, rho = -0.501763
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.076547
obj = -2.967687, rho = -0.537541
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.065458
obj = -3.076814, rho = -0.659372
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.053275
obj = -3.158132, rho = -0.715137
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.043403
obj = -3.235726, rho = -0.715775
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.035394
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.027776
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.021797
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.017106
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.013424
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.010534
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.008267
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.006488
obj = -3.244133, rho = -0.754808
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.237263
obj = -1.619325, rho = -0.502843
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.210298
obj = -1.844180, rho = -0.511575
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.189104
obj = -2.111402, rho = -0.585308
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.165028
obj = -2.420597, rho = -0.623357
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.144505
obj = -2.811194, rho = -0.618937
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.131670
obj = -3.292492, rho = -0.715294
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.123046
obj = -3.839223, rho = -0.930158
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.114459
obj = -4.458733, rho = -0.955806
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.103240
obj = -5.158229, rho = -0.936365
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.092555
obj = -6.010894, rho = -1.012196
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 289
nu = 0.083155
obj = -7.034739, rho = -1.073191
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.077065
obj = -8.279057, rho = -1.216770
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 149
nu = 0.074861
obj = -9.623540, rho = -1.615710
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.072639
obj = -10.903274, rho = -2.065705
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 331
nu = 0.069665
obj = -11.870573, rho = -2.469989
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.....*......*
optimization finished, #iter = 1149
nu = 0.061348
obj = -12.329355, rho = -2.692222
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
......*..*
optimization finished, #iter = 845
nu = 0.051238
obj = -12.528579, rho = -2.861830
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
......*
optimization finished, #iter = 668
nu = 0.040835
obj = -12.575592, rho = -2.913503
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
......*
optimization finished, #iter = 668
nu = 0.032046
obj = -12.575592, rho = -2.913503
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
......*
optimization finished, #iter = 668
nu = 0.025148
obj = -12.575592, rho = -2.913503
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.217915
obj = -1.411705, rho = -0.281225
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.190302
obj = -1.579419, rho = -0.243997
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.166240
obj = -1.769346, rho = -0.200592
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.149231
obj = -1.980117, rho = -0.179438
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 182
nu = 0.134512
obj = -2.192319, rho = -0.232524
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*...*
optimization finished, #iter = 546
nu = 0.115811
obj = -2.392320, rho = -0.262001
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.*
optimization finished, #iter = 423
nu = 0.098633
obj = -2.632857, rho = -0.227420
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*...*
optimization finished, #iter = 712
nu = 0.083544
obj = -2.905580, rho = -0.233039
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*......*
optimization finished, #iter = 973
nu = 0.072066
obj = -3.241731, rho = -0.203810
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.....*
optimization finished, #iter = 700
nu = 0.064849
obj = -3.600223, rho = -0.150990
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*.*..*
optimization finished, #iter = 603
nu = 0.054631
obj = -3.993117, rho = -0.142325
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.046418
obj = -4.488875, rho = -0.142171
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.040486
obj = -5.116783, rho = -0.129900
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*.*
optimization finished, #iter = 492
nu = 0.037151
obj = -5.820313, rho = -0.061629
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*.*
optimization finished, #iter = 477
nu = 0.035342
obj = -6.496079, rho = 0.018756
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
....*
optimization finished, #iter = 446
nu = 0.033548
obj = -6.929532, rho = 0.098441
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.....*........*
optimization finished, #iter = 1311
nu = 0.029162
obj = -7.047358, rho = 0.266860
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.....*........*
optimization finished, #iter = 1311
nu = 0.022885
obj = -7.047358, rho = 0.266860
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.....*........*
optimization finished, #iter = 1311
nu = 0.017959
obj = -7.047358, rho = 0.266860
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.....*........*
optimization finished, #iter = 1311
nu = 0.014094
obj = -7.047358, rho = 0.266860
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.249702
obj = -1.779804, rho = 0.093436
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 97% (97/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.224105
obj = -2.059915, rho = 0.074449
nSV = 28, nBSV = 17
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.200249
obj = -2.402413, rho = 0.024042
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*..*
optimization finished, #iter = 456
nu = 0.182287
obj = -2.812468, rho = 0.009828
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.162738
obj = -3.317818, rho = 0.017124
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*
optimization finished, #iter = 337
nu = 0.148234
obj = -3.954116, rho = 0.011495
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.....*
optimization finished, #iter = 626
nu = 0.138725
obj = -4.737884, rho = 0.006891
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.131507
obj = -5.671063, rho = -0.003436
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*...*
optimization finished, #iter = 530
nu = 0.121520
obj = -6.800457, rho = 0.028358
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
........*...*
optimization finished, #iter = 1193
nu = 0.113666
obj = -8.185924, rho = 0.032656
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
........*.*
optimization finished, #iter = 981
nu = 0.107096
obj = -9.894432, rho = 0.044597
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.098818
obj = -12.001978, rho = 0.047294
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.093090
obj = -14.676657, rho = 0.036911
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.090654
obj = -17.936286, rho = -0.038865
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.089728
obj = -21.761868, rho = -0.159946
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*.*
optimization finished, #iter = 407
nu = 0.086899
obj = -26.109608, rho = -0.269056
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.......*......*
optimization finished, #iter = 1349
nu = 0.081160
obj = -31.137583, rho = -0.329302
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...........*....*
optimization finished, #iter = 1535
nu = 0.075290
obj = -37.404121, rho = -0.409548
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
........*..*
optimization finished, #iter = 1004
nu = 0.071181
obj = -45.055689, rho = -0.538612
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.........*.*
optimization finished, #iter = 1058
nu = 0.068898
obj = -53.950423, rho = -0.774677
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.223964
obj = -1.590310, rho = -0.010208
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.201115
obj = -1.838035, rho = 0.110465
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.182516
obj = -2.131795, rho = 0.212810
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.170280
obj = -2.463946, rho = 0.296137
nSV = 19, nBSV = 15
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.154510
obj = -2.826804, rho = 0.434998
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.136042
obj = -3.244830, rho = 0.474245
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.123791
obj = -3.752088, rho = 0.477575
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.113000
obj = -4.316622, rho = 0.401818
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.105418
obj = -4.934414, rho = 0.259073
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.091873
obj = -5.614959, rho = 0.242534
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.083831
obj = -6.394662, rho = 0.182039
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.078902
obj = -7.178778, rho = 0.036577
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.069399
obj = -7.923116, rho = -0.004916
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.061302
obj = -8.666260, rho = -0.071507
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.053099
obj = -9.406046, rho = -0.153117
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.047075
obj = -10.062757, rho = -0.227528
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.042354
obj = -10.412509, rho = -0.322355
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.033830
obj = -10.417917, rho = -0.335046
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.026548
obj = -10.417917, rho = -0.335046
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 252
nu = 0.020834
obj = -10.417917, rho = -0.335046
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.214622
obj = -1.417554, rho = -0.413498
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.184437
obj = -1.602379, rho = -0.445416
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.169947
obj = -1.818729, rho = -0.437159
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.148547
obj = -2.049708, rho = -0.489559
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.130047
obj = -2.316343, rho = -0.518835
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.115009
obj = -2.641540, rho = -0.461502
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.104287
obj = -2.990697, rho = -0.376035
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.093130
obj = -3.382509, rho = -0.480556
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.083634
obj = -3.797052, rho = -0.703720
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*
optimization finished, #iter = 257
nu = 0.071645
obj = -4.273820, rho = -0.720756
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 287
nu = 0.062009
obj = -4.872773, rho = -0.729408
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*
optimization finished, #iter = 280
nu = 0.054011
obj = -5.625286, rho = -0.722608
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
..*
optimization finished, #iter = 267
nu = 0.049176
obj = -6.548184, rho = -0.802567
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*
optimization finished, #iter = 262
nu = 0.045229
obj = -7.607907, rho = -0.945681
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 356
nu = 0.040116
obj = -8.856200, rho = -1.018952
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 397
nu = 0.035874
obj = -10.427092, rho = -1.114436
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 309
nu = 0.032991
obj = -12.372957, rho = -1.174289
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
...*
optimization finished, #iter = 348
nu = 0.031262
obj = -14.667351, rho = -1.235179
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*...*
optimization finished, #iter = 432
nu = 0.028326
obj = -17.364250, rho = -1.268463
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.026804
obj = -20.672278, rho = -1.500221
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.225645
obj = -1.535213, rho = 0.235591
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.200642
obj = -1.747374, rho = 0.276855
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.176433
obj = -1.998908, rho = 0.286870
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*..*
optimization finished, #iter = 221
nu = 0.156919
obj = -2.298887, rho = 0.345145
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.139805
obj = -2.655828, rho = 0.372761
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.125597
obj = -3.093151, rho = 0.381324
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.114854
obj = -3.610650, rho = 0.422468
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 195
nu = 0.102079
obj = -4.237028, rho = 0.445288
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*...*
optimization finished, #iter = 585
nu = 0.094954
obj = -5.004099, rho = 0.593087
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*
optimization finished, #iter = 397
nu = 0.087732
obj = -5.899635, rho = 0.630218
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.....*
optimization finished, #iter = 533
nu = 0.080500
obj = -6.973740, rho = 0.575790
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 289
nu = 0.074714
obj = -8.268196, rho = 0.543555
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.070144
obj = -9.783962, rho = 0.548806
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.064975
obj = -11.562208, rho = 0.489506
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.061807
obj = -13.599415, rho = 0.440839
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
...*.*
optimization finished, #iter = 446
nu = 0.057505
obj = -15.820523, rho = 0.416268
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.051076
obj = -18.498018, rho = 0.480365
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.046615
obj = -21.810251, rho = 0.629216
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*
optimization finished, #iter = 125
nu = 0.045201
obj = -25.556539, rho = 0.895006
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.044319
obj = -29.221704, rho = 1.270934
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.216455
obj = -1.499402, rho = 0.137401
nSV = 23, nBSV = 19
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.195649
obj = -1.707671, rho = 0.134000
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.176560
obj = -1.947817, rho = 0.212839
nSV = 20, nBSV = 15
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.156587
obj = -2.211614, rho = 0.297251
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.142574
obj = -2.516394, rho = 0.311802
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.131001
obj = -2.822369, rho = 0.355270
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.113689
obj = -3.140172, rho = 0.353392
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 190
nu = 0.097215
obj = -3.519714, rho = 0.352974
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.086584
obj = -3.970100, rho = 0.349521
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.079188
obj = -4.447370, rho = 0.298689
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.074307
obj = -4.845146, rho = 0.166267
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*..*
optimization finished, #iter = 449
nu = 0.063973
obj = -5.101775, rho = 0.051210
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*....*
optimization finished, #iter = 610
nu = 0.051888
obj = -5.369505, rho = 0.036152
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.043639
obj = -5.667924, rho = -0.070899
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.039168
obj = -5.829400, rho = -0.313963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.030737
obj = -5.829400, rho = -0.313963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.024121
obj = -5.829400, rho = -0.313963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.018930
obj = -5.829400, rho = -0.313963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.014855
obj = -5.829400, rho = -0.313963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.011658
obj = -5.829400, rho = -0.313963
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 20
nu = 0.200000
obj = -1.394878, rho = -0.016606
nSV = 22, nBSV = 19
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.183262
obj = -1.596702, rho = 0.039377
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.168418
obj = -1.803360, rho = -0.022611
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.148126
obj = -2.028652, rho = 0.061810
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.127905
obj = -2.295455, rho = 0.078653
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.116272
obj = -2.602680, rho = -0.053575
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.103880
obj = -2.928357, rho = -0.123283
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.095669
obj = -3.259144, rho = -0.251417
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 315
nu = 0.082480
obj = -3.585506, rho = -0.357864
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 372
nu = 0.071934
obj = -3.954185, rho = -0.362651
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.067866
obj = -4.260678, rho = -0.487298
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*.*
optimization finished, #iter = 412
nu = 0.059603
obj = -4.367921, rho = -0.385989
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.047722
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.037451
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.029390
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.023064
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.018100
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.014204
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.011147
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.008747
obj = -4.373538, rho = -0.303212
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.233046
obj = -1.538442, rho = -0.224952
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.205558
obj = -1.726453, rho = -0.208371
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.177108
obj = -1.951289, rho = -0.201876
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.159521
obj = -2.212431, rho = -0.129152
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.140970
obj = -2.497672, rho = -0.061665
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.123754
obj = -2.831051, rho = -0.111595
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.107976
obj = -3.236821, rho = -0.119239
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.**.*
optimization finished, #iter = 151
nu = 0.096441
obj = -3.732093, rho = -0.026139
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.088275
obj = -4.296709, rho = -0.005262
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*..*
optimization finished, #iter = 339
nu = 0.080444
obj = -4.904900, rho = -0.001933
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 297
nu = 0.070717
obj = -5.632530, rho = 0.038722
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.062207
obj = -6.527591, rho = 0.022735
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.055991
obj = -7.627638, rho = -0.024321
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.052943
obj = -8.916267, rho = -0.122148
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.048825
obj = -10.262960, rho = -0.202696
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.044684
obj = -11.822701, rho = -0.146625
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.043131
obj = -13.382332, rho = 0.124419
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.041524
obj = -14.560109, rho = 0.465402
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.038076
obj = -14.943848, rho = 0.565135
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.029880
obj = -14.943848, rho = 0.565135
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*.*
optimization finished, #iter = 150
nu = 0.183011
obj = -1.182113, rho = -0.129398
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.....*
optimization finished, #iter = 569
nu = 0.159440
obj = -1.321025, rho = -0.158860
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.142600
obj = -1.476942, rho = -0.135859
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.122561
obj = -1.645039, rho = -0.173797
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*....*
optimization finished, #iter = 519
nu = 0.107792
obj = -1.838401, rho = -0.091630
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.093270
obj = -2.061124, rho = -0.052692
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.087954
obj = -2.282901, rho = 0.105672
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.077510
obj = -2.469331, rho = 0.058912
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.065417
obj = -2.642132, rho = 0.019889
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.055441
obj = -2.814838, rho = -0.013584
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.049524
obj = -2.966326, rho = -0.126593
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.040340
obj = -3.028074, rho = -0.139349
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.033339
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.026163
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.020532
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.016113
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.012645
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.009923
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.007787
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.006111
obj = -3.055742, rho = -0.095273
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.186562
obj = -1.156188, rho = -0.012295
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.163060
obj = -1.262008, rho = -0.039831
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.136905
obj = -1.379593, rho = -0.045356
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.116855
obj = -1.518370, rho = -0.061281
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.103362
obj = -1.669710, rho = -0.046001
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.086573
obj = -1.835866, rho = -0.008536
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.079407
obj = -2.023712, rho = 0.135300
nSV = 11, nBSV = 6
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.072682
obj = -2.140562, rho = -0.016404
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.062369
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.048944
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.038410
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.030142
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.023655
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.018563
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.014568
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.011432
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.008971
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.007040
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.005525
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.004336
obj = -2.167862, rho = -0.186565
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.187497
obj = -1.302309, rho = -0.278264
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.168997
obj = -1.495765, rho = -0.242244
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.152185
obj = -1.711471, rho = -0.165905
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.136603
obj = -1.962234, rho = -0.235323
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 294
nu = 0.125113
obj = -2.237442, rho = -0.365213
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.111899
obj = -2.542879, rho = -0.450891
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.105695
obj = -2.850141, rho = -0.372179
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.094224
obj = -3.116302, rho = -0.433885
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.079671
obj = -3.402211, rho = -0.460897
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 219
nu = 0.067778
obj = -3.737096, rho = -0.457855
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*..*
optimization finished, #iter = 308
nu = 0.060257
obj = -4.080663, rho = -0.331005
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*
optimization finished, #iter = 350
nu = 0.050826
obj = -4.442253, rho = -0.319306
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 236
nu = 0.044191
obj = -4.824676, rho = -0.525540
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*
optimization finished, #iter = 247
nu = 0.038392
obj = -5.179408, rho = -0.769608
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.034292
obj = -5.458727, rho = -1.138998
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
...*.....*
optimization finished, #iter = 878
nu = 0.029019
obj = -5.502539, rho = -1.377869
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*.....*
optimization finished, #iter = 878
nu = 0.022773
obj = -5.502539, rho = -1.377869
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*.....*
optimization finished, #iter = 878
nu = 0.017871
obj = -5.502539, rho = -1.377869
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*.....*
optimization finished, #iter = 878
nu = 0.014025
obj = -5.502539, rho = -1.377869
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*.....*
optimization finished, #iter = 878
nu = 0.011006
obj = -5.502539, rho = -1.377869
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.209898
obj = -1.380456, rho = -0.014920
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.190349
obj = -1.542062, rho = 0.060943
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.170536
obj = -1.697642, rho = 0.022333
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*..*
optimization finished, #iter = 483
nu = 0.144998
obj = -1.860090, rho = 0.004460
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.123973
obj = -2.048570, rho = 0.027702
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.105238
obj = -2.264317, rho = 0.060708
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.090325
obj = -2.530172, rho = 0.085973
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.080984
obj = -2.832647, rho = 0.074059
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.075041
obj = -3.112207, rho = 0.033242
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.064185
obj = -3.362512, rho = 0.071998
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.055634
obj = -3.603471, rho = 0.249630
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.049200
obj = -3.784090, rho = 0.492396
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.041908
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.032887
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.025809
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.020254
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.015894
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.012473
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.009788
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 366
nu = 0.007682
obj = -3.840204, rho = 0.596298
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.197109
obj = -1.369095, rho = -0.437958
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*..*
optimization finished, #iter = 231
nu = 0.179503
obj = -1.559003, rho = -0.385845
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.162099
obj = -1.771845, rho = -0.386004
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.141787
obj = -2.011677, rho = -0.392946
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.123784
obj = -2.309629, rho = -0.396313
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.115903
obj = -2.642895, rho = -0.426621
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*....*
optimization finished, #iter = 510
nu = 0.107153
obj = -2.973773, rho = -0.533766
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.094544
obj = -3.330419, rho = -0.548140
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.087389
obj = -3.649261, rho = -0.514065
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.**.*
optimization finished, #iter = 194
nu = 0.081149
obj = -3.877814, rho = -0.734678
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
....*..*
optimization finished, #iter = 606
nu = 0.066721
obj = -4.007289, rho = -0.831886
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*
optimization finished, #iter = 387
nu = 0.053879
obj = -4.117383, rho = -0.831360
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 284
nu = 0.043285
obj = -4.242538, rho = -0.837039
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.037044
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.029071
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.022814
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.017903
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.014050
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.011026
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 330
nu = 0.008653
obj = -4.325948, rho = -0.745842
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 183
nu = 0.157975
obj = -0.965799, rho = 0.179891
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.131799
obj = -1.061883, rho = 0.182210
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.113488
obj = -1.175563, rho = 0.235636
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.100960
obj = -1.298200, rho = 0.272232
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.088009
obj = -1.425494, rho = 0.262814
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.078317
obj = -1.548193, rho = 0.239743
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.067063
obj = -1.665724, rho = 0.225572
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.058200
obj = -1.767571, rho = 0.212172
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.049782
obj = -1.832495, rho = 0.214012
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.040551
obj = -1.872077, rho = 0.218838
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.033453
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.026252
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.020602
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.016168
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.012688
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.009957
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.007814
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.006132
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.004812
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.003776
obj = -1.888233, rho = 0.229230
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.194110
obj = -1.243116, rho = 0.031117
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.168795
obj = -1.382813, rho = 0.029507
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.149011
obj = -1.534034, rho = 0.007104
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.138802
obj = -1.674270, rho = 0.040837
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.119208
obj = -1.775006, rho = -0.011507
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.097132
obj = -1.885484, rho = -0.012913
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.080497
obj = -2.021355, rho = -0.020530
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*...*
optimization finished, #iter = 480
nu = 0.069108
obj = -2.159321, rho = -0.012271
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.057927
obj = -2.297257, rho = -0.000037
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.049156
obj = -2.426248, rho = 0.034691
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.......*
optimization finished, #iter = 866
nu = 0.042350
obj = -2.522864, rho = 0.106590
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.035606
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.027942
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.021928
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.017208
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.013504
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.010598
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.008317
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.006526
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.005122
obj = -2.560952, rho = 0.108203
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.207362
obj = -1.399163, rho = -0.202830
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 97% (97/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.181342
obj = -1.592345, rho = -0.187119
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.166416
obj = -1.813979, rho = -0.188861
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.148197
obj = -2.044553, rho = -0.139676
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.129470
obj = -2.312445, rho = -0.109156
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.112828
obj = -2.639174, rho = -0.110415
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.104990
obj = -3.023287, rho = -0.003617
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.097747
obj = -3.379139, rho = 0.080195
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.087441
obj = -3.714942, rho = 0.178258
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.073871
obj = -4.063329, rho = 0.195954
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.063354
obj = -4.468941, rho = 0.275297
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.053769
obj = -4.943462, rho = 0.310364
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 212
nu = 0.047710
obj = -5.497637, rho = 0.423130
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.042020
obj = -6.005286, rho = 0.512273
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 290
nu = 0.036767
obj = -6.517823, rho = 0.559822
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.033408
obj = -6.961335, rho = 0.503429
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.029151
obj = -7.045799, rho = 0.512265
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.022876
obj = -7.045799, rho = 0.512265
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.017952
obj = -7.045799, rho = 0.512265
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.014088
obj = -7.045799, rho = 0.512265
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.160882
obj = -1.041600, rho = 0.022412
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.142654
obj = -1.162041, rho = 0.048632
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.124199
obj = -1.290447, rho = 0.112071
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.109665
obj = -1.434738, rho = 0.147163
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 320
nu = 0.095235
obj = -1.581862, rho = 0.187610
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*
optimization finished, #iter = 277
nu = 0.081161
obj = -1.754368, rho = 0.191667
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.068838
obj = -1.967089, rho = 0.195076
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
...*....*
optimization finished, #iter = 767
nu = 0.062495
obj = -2.214247, rho = 0.232545
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.055359
obj = -2.459313, rho = 0.261418
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*..*
optimization finished, #iter = 413
nu = 0.050388
obj = -2.707657, rho = 0.318404
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*..*
optimization finished, #iter = 380
nu = 0.046946
obj = -2.889868, rho = 0.393071
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.040863
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.032068
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.025165
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.019749
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.015498
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.012162
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.009544
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.007490
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.............*....*
optimization finished, #iter = 1770
nu = 0.005878
obj = -2.939493, rho = 0.481926
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.157404
obj = -1.011541, rho = -0.419764
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.139050
obj = -1.123188, rho = -0.466648
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.127320
obj = -1.229845, rho = -0.482939
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.109761
obj = -1.324086, rho = -0.469137
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.094528
obj = -1.409764, rho = -0.472762
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.078009
obj = -1.494224, rho = -0.474044
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 391
nu = 0.064495
obj = -1.588380, rho = -0.485726
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.054883
obj = -1.690330, rho = -0.511729
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.045609
obj = -1.781640, rho = -0.501255
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.039052
obj = -1.866446, rho = -0.465031
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.033848
obj = -1.910713, rho = -0.451570
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.026565
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.020847
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.016360
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.012838
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.010075
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.007907
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.006205
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.004869
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.003821
obj = -1.910713, rho = -0.451571
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.216783
obj = -1.564205, rho = -0.208060
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.197243
obj = -1.818915, rho = -0.217384
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.181135
obj = -2.111317, rho = -0.191532
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*....*
optimization finished, #iter = 649
nu = 0.161109
obj = -2.456335, rho = -0.113035
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.147306
obj = -2.876397, rho = -0.096542
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.134226
obj = -3.371751, rho = -0.160352
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.123127
obj = -3.959218, rho = -0.236502
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.115758
obj = -4.639642, rho = -0.325272
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.105813
obj = -5.415225, rho = -0.339889
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.098899
obj = -6.306232, rho = -0.281766
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*..*
optimization finished, #iter = 474
nu = 0.090716
obj = -7.284141, rho = -0.216201
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.079607
obj = -8.467701, rho = -0.209594
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.**....*
optimization finished, #iter = 570
nu = 0.070726
obj = -9.970339, rho = -0.210398
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.063691
obj = -11.884274, rho = -0.210050
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.....*
optimization finished, #iter = 608
nu = 0.058176
obj = -14.323066, rho = -0.209977
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.....*
optimization finished, #iter = 566
nu = 0.053978
obj = -17.428650, rho = -0.172465
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.050907
obj = -21.348681, rho = -0.036273
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.048437
obj = -26.274130, rho = 0.014445
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.046541
obj = -32.441285, rho = -0.087579
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.045250
obj = -40.104385, rho = -0.298725
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.251902
obj = -1.746204, rho = -0.056774
nSV = 30, nBSV = 23
Total nSV = 30
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.227927
obj = -1.993681, rho = -0.012888
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.201054
obj = -2.279085, rho = 0.008531
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.178279
obj = -2.630168, rho = 0.004394
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.159756
obj = -3.045929, rho = 0.005621
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 96% (96/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*
optimization finished, #iter = 294
nu = 0.144416
obj = -3.540427, rho = 0.015728
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 402
nu = 0.129083
obj = -4.143422, rho = 0.023885
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
........*
optimization finished, #iter = 848
nu = 0.116088
obj = -4.892955, rho = 0.053177
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.................*
optimization finished, #iter = 1950
nu = 0.105481
obj = -5.838057, rho = 0.065208
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 96% (96/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*....*
optimization finished, #iter = 736
nu = 0.098169
obj = -7.010501, rho = 0.082230
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 96% (96/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.........*
optimization finished, #iter = 1278
nu = 0.091308
obj = -8.432852, rho = 0.086841
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*....*
optimization finished, #iter = 908
nu = 0.084869
obj = -10.224406, rho = 0.067905
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 96% (96/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 491
nu = 0.080906
obj = -12.439373, rho = 0.012235
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 97% (97/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 215
nu = 0.078170
obj = -15.104656, rho = -0.121460
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.076065
obj = -18.231039, rho = -0.312328
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
......*....*
optimization finished, #iter = 1060
nu = 0.073770
obj = -21.787692, rho = -0.582623
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
..*..*
optimization finished, #iter = 448
nu = 0.068415
obj = -25.918944, rho = -0.615349
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 95% (950/1000) (classification)
..*.*
optimization finished, #iter = 345
nu = 0.065611
obj = -30.663897, rho = -0.595634
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 94.1% (941/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.061076
obj = -36.168915, rho = -0.947079
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 98% (98/100) (classification)
Accuracy = 93% (930/1000) (classification)
........*
optimization finished, #iter = 874
nu = 0.057667
obj = -42.434621, rho = -1.174071
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 92.4% (924/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.191248
obj = -1.347061, rho = -0.590964
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 140
nu = 0.170784
obj = -1.553074, rho = -0.614607
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.152304
obj = -1.801934, rho = -0.642350
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.139140
obj = -2.100402, rho = -0.635528
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.128543
obj = -2.444949, rho = -0.553422
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.118894
obj = -2.823971, rho = -0.485660
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.108113
obj = -3.248824, rho = -0.500595
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.099116
obj = -3.725093, rho = -0.553592
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.089660
obj = -4.240467, rho = -0.659952
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.083395
obj = -4.791693, rho = -0.784678
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.079261
obj = -5.263702, rho = -0.950991
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*.*
optimization finished, #iter = 326
nu = 0.072919
obj = -5.496855, rho = -1.096005
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 248
nu = 0.058958
obj = -5.603918, rho = -1.111188
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*
optimization finished, #iter = 297
nu = 0.047849
obj = -5.677855, rho = -1.020479
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.9% (949/1000) (classification)
...*...*
optimization finished, #iter = 601
nu = 0.038219
obj = -5.688097, rho = -0.976232
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
...*...*
optimization finished, #iter = 601
nu = 0.029993
obj = -5.688097, rho = -0.976232
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
...*...*
optimization finished, #iter = 601
nu = 0.023537
obj = -5.688097, rho = -0.976232
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
...*...*
optimization finished, #iter = 601
nu = 0.018471
obj = -5.688097, rho = -0.976232
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
...*...*
optimization finished, #iter = 601
nu = 0.014495
obj = -5.688097, rho = -0.976232
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
...*...*
optimization finished, #iter = 601
nu = 0.011375
obj = -5.688097, rho = -0.976232
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.8% (948/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.182896
obj = -1.201510, rho = 0.155392
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.157577
obj = -1.355240, rho = 0.169187
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.136441
obj = -1.547183, rho = 0.168313
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.122605
obj = -1.775798, rho = 0.181054
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.108905
obj = -2.046581, rho = 0.273415
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.100460
obj = -2.358862, rho = 0.358750
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.095834
obj = -2.680249, rho = 0.566840
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*..*
optimization finished, #iter = 408
nu = 0.082758
obj = -3.004134, rho = 0.636775
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 332
nu = 0.073587
obj = -3.401202, rho = 0.710093
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
......*.....*
optimization finished, #iter = 1198
nu = 0.063641
obj = -3.846961, rho = 0.732514
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 273
nu = 0.056188
obj = -4.393904, rho = 0.765454
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.050633
obj = -5.046825, rho = 0.737482
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.048268
obj = -5.708821, rho = 0.655425
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.046411
obj = -6.237130, rho = 0.551105
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.040376
obj = -6.549306, rho = 0.491550
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 375
nu = 0.034655
obj = -6.726837, rho = 0.434519
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.027903
obj = -6.744834, rho = 0.415277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.021897
obj = -6.744834, rho = 0.415277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.017184
obj = -6.744834, rho = 0.415277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.013485
obj = -6.744834, rho = 0.415277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.199974
obj = -1.331988, rho = -0.114617
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.176598
obj = -1.502126, rho = -0.146756
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.156225
obj = -1.700903, rho = -0.187274
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*.*
optimization finished, #iter = 280
nu = 0.136161
obj = -1.929349, rho = -0.171442
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.120035
obj = -2.200871, rho = -0.173823
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.108209
obj = -2.525670, rho = -0.171185
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.102109
obj = -2.871143, rho = -0.162666
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.096807
obj = -3.169064, rho = -0.108434
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.084883
obj = -3.416458, rho = -0.032458
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.073444
obj = -3.606766, rho = 0.076436
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.063097
obj = -3.721778, rho = -0.018820
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 381
nu = 0.050153
obj = -3.808950, rho = -0.029853
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 303
nu = 0.040216
obj = -3.919095, rho = -0.032735
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.034022
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.026699
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.020953
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.016443
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.012904
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.010126
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.007947
obj = -3.974298, rho = -0.092526
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.178562
obj = -1.225157, rho = -0.091639
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.159623
obj = -1.399090, rho = -0.138139
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.145365
obj = -1.593095, rho = -0.250574
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.130731
obj = -1.802751, rho = -0.354079
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.115669
obj = -2.030717, rho = -0.413497
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.105235
obj = -2.267316, rho = -0.537125
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.092101
obj = -2.523008, rho = -0.630657
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.078712
obj = -2.821211, rho = -0.620315
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.071009
obj = -3.167187, rho = -0.639129
nSV = 9, nBSV = 5
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.066515
obj = -3.465162, rho = -0.696407
nSV = 9, nBSV = 4
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.058242
obj = -3.670510, rho = -0.721297
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 274
nu = 0.048790
obj = -3.840161, rho = -0.672612
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*....*
optimization finished, #iter = 412
nu = 0.039555
obj = -4.006541, rho = -0.655162
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.034790
obj = -4.158103, rho = -0.558809
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.027960
obj = -4.161489, rho = -0.534202
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.021942
obj = -4.161489, rho = -0.534202
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.017219
obj = -4.161489, rho = -0.534202
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.013513
obj = -4.161489, rho = -0.534202
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.010604
obj = -4.161489, rho = -0.534202
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.008322
obj = -4.161489, rho = -0.534202
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.178770
obj = -1.185877, rho = -0.106571
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.159247
obj = -1.336655, rho = -0.231826
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.139376
obj = -1.504730, rho = -0.385926
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.125048
obj = -1.696177, rho = -0.470357
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 286
nu = 0.112318
obj = -1.886880, rho = -0.477409
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 253
nu = 0.098112
obj = -2.093468, rho = -0.439727
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 196
nu = 0.084559
obj = -2.322994, rho = -0.552362
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.072136
obj = -2.598860, rho = -0.595475
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.**..*
optimization finished, #iter = 310
nu = 0.062403
obj = -2.936708, rho = -0.682023
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.054910
obj = -3.342039, rho = -0.814799
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*
optimization finished, #iter = 484
nu = 0.049062
obj = -3.825653, rho = -0.914831
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 248
nu = 0.046182
obj = -4.336071, rho = -1.089134
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*..*
optimization finished, #iter = 368
nu = 0.040303
obj = -4.841557, rho = -1.115000
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 327
nu = 0.034606
obj = -5.461501, rho = -1.076189
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.031075
obj = -6.199421, rho = -1.087945
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.029353
obj = -6.934395, rho = -1.272205
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.028316
obj = -7.437386, rho = -1.624581
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.024362
obj = -7.503617, rho = -1.807276
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.019119
obj = -7.503617, rho = -1.807276
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 217
nu = 0.015003
obj = -7.503617, rho = -1.807276
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.196012
obj = -1.295683, rho = -0.145345
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.172318
obj = -1.454400, rho = -0.092023
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.158728
obj = -1.633160, rho = 0.013476
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.135813
obj = -1.811719, rho = 0.029149
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.117758
obj = -2.029419, rho = -0.037118
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.102863
obj = -2.270362, rho = -0.004499
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.087931
obj = -2.566354, rho = 0.007810
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.076160
obj = -2.939514, rho = 0.014236
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 267
nu = 0.067722
obj = -3.400710, rho = 0.017240
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.062443
obj = -3.942031, rho = 0.091079
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 264
nu = 0.058903
obj = -4.522668, rho = 0.127861
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.053708
obj = -5.136852, rho = 0.134864
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.050216
obj = -5.716168, rho = 0.095286
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.042892
obj = -6.290280, rho = 0.002676
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.039736
obj = -6.868258, rho = 0.078110
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.037529
obj = -7.115639, rho = 0.189922
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.029451
obj = -7.115639, rho = 0.189922
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.023112
obj = -7.115639, rho = 0.189922
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.018137
obj = -7.115639, rho = 0.189922
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.014234
obj = -7.115639, rho = 0.189922
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.201221
obj = -1.290909, rho = 0.083099
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.175333
obj = -1.435368, rho = 0.124764
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 179
nu = 0.148936
obj = -1.607541, rho = 0.137936
nSV = 22, nBSV = 11
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.134118
obj = -1.811295, rho = 0.052359
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.118311
obj = -2.028734, rho = -0.070618
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.103659
obj = -2.270606, rho = -0.204448
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.093301
obj = -2.533348, rho = -0.362773
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.080838
obj = -2.808580, rho = -0.279783
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.069673
obj = -3.120966, rho = -0.290435
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.061144
obj = -3.485845, rho = -0.082762
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.052393
obj = -3.898928, rho = 0.092642
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.045196
obj = -4.405707, rho = 0.209617
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.039558
obj = -5.023310, rho = 0.355319
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 127
nu = 0.035225
obj = -5.764361, rho = 0.541884
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 339
nu = 0.032196
obj = -6.595148, rho = 0.633871
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 311
nu = 0.029683
obj = -7.498865, rho = 0.598729
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*
optimization finished, #iter = 267
nu = 0.028085
obj = -8.358126, rho = 0.662459
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*.*
optimization finished, #iter = 468
nu = 0.027155
obj = -8.900044, rho = 0.967021
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
...*.*
optimization finished, #iter = 470
nu = 0.022790
obj = -8.944061, rho = 1.077465
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
...*.*
optimization finished, #iter = 470
nu = 0.017885
obj = -8.944061, rho = 1.077465
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.221081
obj = -1.512531, rho = -0.123550
nSV = 28, nBSV = 19
Total nSV = 28
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.197411
obj = -1.722601, rho = -0.058028
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.173286
obj = -1.972232, rho = -0.058551
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*..*
optimization finished, #iter = 206
nu = 0.155807
obj = -2.269395, rho = 0.027405
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.141137
obj = -2.609777, rho = 0.072131
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.133815
obj = -2.972011, rho = 0.162184
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.124018
obj = -3.304869, rho = 0.214045
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.107127
obj = -3.631021, rho = 0.283325
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 286
nu = 0.090883
obj = -4.004629, rho = 0.252731
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*.*
optimization finished, #iter = 383
nu = 0.082182
obj = -4.391406, rho = 0.374628
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*.*
optimization finished, #iter = 353
nu = 0.071743
obj = -4.745487, rho = 0.377854
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
...*.*
optimization finished, #iter = 419
nu = 0.062555
obj = -5.055820, rho = 0.199709
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*.....*...............*
optimization finished, #iter = 2173
nu = 0.054817
obj = -5.249368, rho = -0.033306
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.045705
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.035868
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.028147
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.022089
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.017335
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.013603
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*...*
optimization finished, #iter = 714
nu = 0.010675
obj = -5.338512, rho = -0.018355
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.167233
obj = -1.070141, rho = 0.254613
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.143572
obj = -1.192828, rho = 0.242424
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.124390
obj = -1.341588, rho = 0.256193
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.113307
obj = -1.509278, rho = 0.292015
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.099202
obj = -1.677809, rho = 0.313985
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.084696
obj = -1.876553, rho = 0.323653
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.073236
obj = -2.119799, rho = 0.336476
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.067936
obj = -2.395108, rho = 0.365458
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.063894
obj = -2.631821, rho = 0.409252
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.057693
obj = -2.782796, rho = 0.487571
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.048276
obj = -2.866697, rho = 0.509876
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.038902
obj = -2.934644, rho = 0.486625
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.031419
obj = -2.996300, rho = 0.485825
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.025747
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.020205
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.015856
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.012443
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.009765
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.007663
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.006014
obj = -3.007076, rho = 0.506384
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*...*
optimization finished, #iter = 304
nu = 0.169999
obj = -1.066333, rho = -0.157555
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.146902
obj = -1.177621, rho = -0.160937
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*.*
optimization finished, #iter = 124
nu = 0.126594
obj = -1.300323, rho = -0.157009
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.111087
obj = -1.434597, rho = -0.101769
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.097056
obj = -1.578211, rho = -0.128293
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.087661
obj = -1.700932, rho = -0.219760
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.073413
obj = -1.811909, rho = -0.283383
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 280
nu = 0.061189
obj = -1.933594, rho = -0.319990
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.054876
obj = -2.038912, rho = -0.440657
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.046706
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.036653
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.028764
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.022572
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.017714
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.013901
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.010909
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.008561
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.006718
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.005272
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.004137
obj = -2.068935, rho = -0.536446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.198415
obj = -1.321061, rho = 0.028500
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 340
nu = 0.172900
obj = -1.496987, rho = 0.034973
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.158308
obj = -1.691353, rho = -0.026392
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.140173
obj = -1.895493, rho = -0.050611
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.124485
obj = -2.130832, rho = -0.083397
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.108714
obj = -2.381342, rho = -0.076433
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.096020
obj = -2.666871, rho = -0.135642
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.083219
obj = -2.984874, rho = -0.270047
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.072619
obj = -3.361441, rho = -0.361845
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*........*
optimization finished, #iter = 1024
nu = 0.062630
obj = -3.809766, rho = -0.386767
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.054275
obj = -4.376614, rho = -0.380508
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.047816
obj = -5.092545, rho = -0.378746
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.044953
obj = -5.947874, rho = -0.482247
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.043200
obj = -6.835365, rho = -0.633052
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*..*
optimization finished, #iter = 400
nu = 0.040199
obj = -7.637828, rho = -0.763937
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 401
nu = 0.034562
obj = -8.518550, rho = -0.731315
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.030993
obj = -9.523161, rho = -0.718002
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.029675
obj = -10.371736, rho = -0.848391
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.026969
obj = -10.584921, rho = -0.988526
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.021164
obj = -10.584921, rho = -0.988526
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.208225
obj = -1.321824, rho = 0.218695
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.176696
obj = -1.472627, rho = 0.216638
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.150489
obj = -1.662684, rho = 0.224115
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.140000
obj = -1.887783, rho = 0.399343
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.124661
obj = -2.095586, rho = 0.496739
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 281
nu = 0.110061
obj = -2.322567, rho = 0.460198
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.095421
obj = -2.565777, rho = 0.473292
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.083822
obj = -2.825161, rho = 0.533522
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.072322
obj = -3.091503, rho = 0.559058
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*..*
optimization finished, #iter = 373
nu = 0.062433
obj = -3.383443, rho = 0.523876
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.056596
obj = -3.648744, rho = 0.713595
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.050305
obj = -3.815796, rho = 1.023255
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.041871
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.032859
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.025786
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.020236
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.015880
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.012462
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.009780
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.007675
obj = -3.837342, rho = 1.218878
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.192586
obj = -1.246260, rho = 0.161768
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.167010
obj = -1.392142, rho = 0.145299
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.146957
obj = -1.558544, rho = 0.191811
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.128326
obj = -1.747849, rho = 0.169493
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 292
nu = 0.110266
obj = -1.974495, rho = 0.175065
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.096598
obj = -2.257748, rho = 0.179571
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.087876
obj = -2.578389, rho = 0.207647
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*...*
optimization finished, #iter = 410
nu = 0.079527
obj = -2.930824, rho = 0.259408
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.....*
optimization finished, #iter = 632
nu = 0.068568
obj = -3.346677, rho = 0.253464
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.061493
obj = -3.862685, rho = 0.213596
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.059282
obj = -4.406021, rho = 0.197583
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 255
nu = 0.055063
obj = -4.873122, rho = 0.303382
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*..*
optimization finished, #iter = 304
nu = 0.048863
obj = -5.288094, rho = 0.465283
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.042980
obj = -5.650004, rho = 0.535786
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.039010
obj = -5.804358, rho = 0.716579
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.030613
obj = -5.804358, rho = 0.716579
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.024024
obj = -5.804358, rho = 0.716579
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.018853
obj = -5.804358, rho = 0.716579
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.014795
obj = -5.804358, rho = 0.716579
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*..*
optimization finished, #iter = 332
nu = 0.011611
obj = -5.804358, rho = 0.716579
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.156126
obj = -0.978169, rho = -0.242857
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.137810
obj = -1.070578, rho = -0.163638
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.117249
obj = -1.171505, rho = -0.172994
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.104659
obj = -1.272342, rho = -0.118879
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.088843
obj = -1.362416, rho = -0.149367
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.075248
obj = -1.450695, rho = -0.139358
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.062704
obj = -1.542031, rho = -0.093720
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.051743
obj = -1.650157, rho = -0.100284
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*...*
optimization finished, #iter = 375
nu = 0.043949
obj = -1.766744, rho = -0.193374
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.......*
optimization finished, #iter = 1088
nu = 0.037575
obj = -1.881839, rho = -0.314865
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.031371
obj = -1.992227, rho = -0.368338
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*
optimization finished, #iter = 239
nu = 0.027230
obj = -2.075176, rho = -0.486581
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.........*
optimization finished, #iter = 1087
nu = 0.022854
obj = -2.104666, rho = -0.587800
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.018027
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.014147
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.011102
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.008712
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.006837
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.005365
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...............*
optimization finished, #iter = 1646
nu = 0.004211
obj = -2.105684, rho = -0.595641
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.188877
obj = -1.342736, rho = -0.222467
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.180000
obj = -1.538924, rho = -0.331169
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.163459
obj = -1.728899, rho = -0.421761
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.144788
obj = -1.924380, rho = -0.497618
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 191
nu = 0.123716
obj = -2.150893, rho = -0.501111
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.108726
obj = -2.413800, rho = -0.567539
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.095524
obj = -2.714344, rho = -0.621831
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.084405
obj = -3.061278, rho = -0.661790
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.077092
obj = -3.429338, rho = -0.707019
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.069717
obj = -3.793946, rho = -0.786113
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.064790
obj = -4.078518, rho = -0.925163
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 476
nu = 0.055280
obj = -4.228318, rho = -0.979533
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
................*..........*
optimization finished, #iter = 2695
nu = 0.044392
obj = -4.356991, rho = -0.982631
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.......*...*
optimization finished, #iter = 1029
nu = 0.036396
obj = -4.484673, rho = -1.020882
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
....*
optimization finished, #iter = 482
nu = 0.030630
obj = -4.558570, rho = -1.063602
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
....*
optimization finished, #iter = 482
nu = 0.024037
obj = -4.558570, rho = -1.063602
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
....*
optimization finished, #iter = 482
nu = 0.018863
obj = -4.558570, rho = -1.063602
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
....*
optimization finished, #iter = 482
nu = 0.014803
obj = -4.558570, rho = -1.063602
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
....*
optimization finished, #iter = 482
nu = 0.011617
obj = -4.558570, rho = -1.063602
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
....*
optimization finished, #iter = 482
nu = 0.009116
obj = -4.558570, rho = -1.063602
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.226521
obj = -1.554115, rho = 0.093162
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.203890
obj = -1.768492, rho = 0.107392
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.188559
obj = -2.001345, rho = -0.057825
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.169365
obj = -2.227162, rho = -0.200669
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*...*
optimization finished, #iter = 617
nu = 0.151188
obj = -2.456470, rho = -0.243180
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
..*.*
optimization finished, #iter = 315
nu = 0.127612
obj = -2.705393, rho = -0.214797
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
...*
optimization finished, #iter = 369
nu = 0.110316
obj = -2.996535, rho = -0.188827
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.094622
obj = -3.330418, rho = -0.207074
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.080192
obj = -3.741180, rho = -0.207790
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.069433
obj = -4.260682, rho = -0.228891
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.064437
obj = -4.845963, rho = -0.140880
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.060985
obj = -5.397737, rho = -0.024145
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.053692
obj = -5.854197, rho = -0.045582
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.049252
obj = -6.225007, rho = -0.370178
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.042241
obj = -6.286763, rho = -0.579415
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.033149
obj = -6.286763, rho = -0.579415
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.026014
obj = -6.286763, rho = -0.579415
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.020415
obj = -6.286763, rho = -0.579415
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.016021
obj = -6.286763, rho = -0.579415
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.012572
obj = -6.286763, rho = -0.579415
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.229277
obj = -1.564508, rho = -0.165854
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.209328
obj = -1.777734, rho = -0.013733
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.182167
obj = -2.006793, rho = 0.022465
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.159732
obj = -2.290652, rho = -0.004658
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.141314
obj = -2.625453, rho = -0.050510
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.126177
obj = -3.036187, rho = -0.063324
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.115743
obj = -3.499964, rho = -0.104972
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.105684
obj = -4.032246, rho = -0.097606
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.098722
obj = -4.598688, rho = -0.127680
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.091144
obj = -5.141606, rho = -0.117177
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.080754
obj = -5.687549, rho = -0.081437
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.072413
obj = -6.210696, rho = -0.032501
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.064663
obj = -6.615863, rho = -0.117098
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.054226
obj = -6.934486, rho = -0.210858
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.046034
obj = -7.167762, rho = -0.331453
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.038042
obj = -7.215882, rho = -0.416432
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.029854
obj = -7.215882, rho = -0.416432
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.023428
obj = -7.215882, rho = -0.416432
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.018385
obj = -7.215882, rho = -0.416432
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.014428
obj = -7.215882, rho = -0.416432
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.211319
obj = -1.403215, rho = 0.133277
nSV = 22, nBSV = 18
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.184319
obj = -1.583915, rho = 0.080017
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.168348
obj = -1.789683, rho = 0.096757
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.149326
obj = -1.997807, rho = 0.105560
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.132395
obj = -2.220375, rho = 0.087160
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.112756
obj = -2.478867, rho = 0.063698
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.099423
obj = -2.788414, rho = 0.037202
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.088946
obj = -3.107716, rho = 0.010935
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.079732
obj = -3.427432, rho = 0.094807
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.067541
obj = -3.776284, rho = 0.156265
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.057117
obj = -4.199028, rho = 0.164424
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.051140
obj = -4.692688, rho = 0.044867
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 360
nu = 0.046915
obj = -5.158356, rho = -0.081992
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.043689
obj = -5.481051, rho = -0.174924
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 381
nu = 0.037143
obj = -5.528318, rho = -0.159640
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 386
nu = 0.029147
obj = -5.528318, rho = -0.159178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 386
nu = 0.022874
obj = -5.528318, rho = -0.159178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 386
nu = 0.017950
obj = -5.528318, rho = -0.159178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 386
nu = 0.014087
obj = -5.528318, rho = -0.159178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 386
nu = 0.011055
obj = -5.528318, rho = -0.159178
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.224898
obj = -1.525233, rho = -0.255937
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.202081
obj = -1.732838, rho = -0.301533
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.179845
obj = -1.963167, rho = -0.284536
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*..*
optimization finished, #iter = 307
nu = 0.164066
obj = -2.207898, rho = -0.268230
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.148886
obj = -2.450077, rho = -0.262687
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.132416
obj = -2.693761, rho = -0.212921
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.116237
obj = -2.910732, rho = -0.158266
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.102874
obj = -3.081137, rho = -0.134524
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 316
nu = 0.084780
obj = -3.211104, rho = -0.135526
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.068185
obj = -3.355728, rho = -0.135336
nSV = 15, nBSV = 3
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
........*..*
optimization finished, #iter = 1040
nu = 0.056528
obj = -3.533568, rho = -0.161548
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.....*
optimization finished, #iter = 565
nu = 0.045860
obj = -3.715666, rho = -0.155228
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.................................*
optimization finished, #iter = 3425
nu = 0.038001
obj = -3.920847, rho = -0.133669
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 369
nu = 0.034455
obj = -4.023281, rho = -0.173305
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.027038
obj = -4.023281, rho = -0.173402
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.021218
obj = -4.023281, rho = -0.173402
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.016651
obj = -4.023281, rho = -0.173402
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.013067
obj = -4.023281, rho = -0.173402
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.010255
obj = -4.023281, rho = -0.173402
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 381
nu = 0.008047
obj = -4.023281, rho = -0.173402
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.168238
obj = -1.053312, rho = -0.128883
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.144748
obj = -1.159172, rho = -0.177154
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.126734
obj = -1.280281, rho = -0.175075
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.110878
obj = -1.404559, rho = -0.224857
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.095523
obj = -1.531443, rho = -0.273988
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.082107
obj = -1.669426, rho = -0.241602
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.071791
obj = -1.805470, rho = -0.266381
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.061467
obj = -1.927507, rho = -0.287208
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.051185
obj = -2.051360, rho = -0.287120
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 145
nu = 0.042134
obj = -2.199823, rho = -0.288429
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.037166
obj = -2.350435, rho = -0.120472
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.033640
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.026399
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.020717
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.016258
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.012758
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.010012
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.007857
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.006166
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 121
nu = 0.004839
obj = -2.419786, rho = 0.189979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.209076
obj = -1.363724, rho = -0.065993
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*
optimization finished, #iter = 353
nu = 0.185685
obj = -1.520184, rho = -0.046701
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.159572
obj = -1.700888, rho = -0.058447
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.139675
obj = -1.914336, rho = -0.070940
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.129795
obj = -2.136752, rho = 0.027561
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 340
nu = 0.115814
obj = -2.327551, rho = 0.111789
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.097153
obj = -2.527798, rho = 0.102690
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*...*
optimization finished, #iter = 470
nu = 0.083878
obj = -2.753731, rho = 0.059514
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 371
nu = 0.073296
obj = -2.976398, rho = 0.064104
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 278
nu = 0.061075
obj = -3.206637, rho = 0.076056
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.053289
obj = -3.433319, rho = 0.195438
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.046812
obj = -3.591583, rho = 0.276561
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.039777
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.031215
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.024496
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.019224
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.015086
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.011839
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.009291
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 263
nu = 0.007291
obj = -3.645825, rho = 0.271417
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.168422
obj = -1.004629, rho = -0.207266
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 268
nu = 0.141170
obj = -1.090023, rho = -0.216596
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*..*
optimization finished, #iter = 285
nu = 0.119634
obj = -1.188791, rho = -0.166540
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.104501
obj = -1.295569, rho = -0.173146
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*...*
optimization finished, #iter = 361
nu = 0.091004
obj = -1.389345, rho = -0.188602
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.075351
obj = -1.489855, rho = -0.179090
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 354
nu = 0.062454
obj = -1.611040, rho = -0.179815
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.052739
obj = -1.757044, rho = -0.194329
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 195
nu = 0.044797
obj = -1.920188, rho = -0.202487
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.038095
obj = -2.115790, rho = -0.194797
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.034439
obj = -2.296099, rho = -0.120194
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.030827
obj = -2.454288, rho = -0.051924
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.027207
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.021351
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.016756
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.013149
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.010319
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.008098
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.006355
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.004987
obj = -2.493992, rho = 0.037167
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.171273
obj = -1.078113, rho = -0.662363
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.150256
obj = -1.191023, rho = -0.742581
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.133649
obj = -1.299627, rho = -0.911039
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.115171
obj = -1.401557, rho = -1.057416
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*....*
optimization finished, #iter = 477
nu = 0.100000
obj = -1.491588, rho = -1.253987
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.085246
obj = -1.570204, rho = -1.373177
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*
optimization finished, #iter = 275
nu = 0.071163
obj = -1.630361, rho = -1.371566
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.057509
obj = -1.682794, rho = -1.389176
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 259
nu = 0.046239
obj = -1.741113, rho = -1.408225
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.038755
obj = -1.796992, rho = -1.441667
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.032164
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.025241
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.019808
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.015545
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.012199
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.009573
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.007513
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.005896
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.004627
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.003631
obj = -1.815600, rho = -1.674986
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.168121
obj = -1.128062, rho = -0.254463
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.147512
obj = -1.278275, rho = -0.238307
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.133086
obj = -1.454213, rho = -0.229986
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.121742
obj = -1.635563, rho = -0.170229
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.108646
obj = -1.813330, rho = -0.075073
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.094711
obj = -2.010994, rho = -0.144704
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.083031
obj = -2.210481, rho = -0.211530
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.070877
obj = -2.442127, rho = -0.231241
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.063323
obj = -2.684742, rho = -0.298848
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.055775
obj = -2.909448, rho = -0.249820
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*.*
optimization finished, #iter = 459
nu = 0.047587
obj = -3.123098, rho = -0.292508
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
...*
optimization finished, #iter = 321
nu = 0.040253
obj = -3.324529, rho = -0.369342
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*
optimization finished, #iter = 288
nu = 0.033690
obj = -3.539782, rho = -0.476407
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
...*
optimization finished, #iter = 368
nu = 0.028776
obj = -3.751507, rho = -0.569249
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*.*
optimization finished, #iter = 279
nu = 0.024778
obj = -3.899297, rho = -0.619631
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.020740
obj = -3.933303, rho = -0.642901
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.016276
obj = -3.933303, rho = -0.642901
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.012772
obj = -3.933303, rho = -0.642901
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.010023
obj = -3.933303, rho = -0.642901
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.007866
obj = -3.933303, rho = -0.642901
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.211608
obj = -1.405165, rho = -0.025008
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.186262
obj = -1.579174, rho = -0.066005
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.162895
obj = -1.786712, rho = -0.127955
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.144381
obj = -2.031264, rho = -0.116554
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*...*.*
optimization finished, #iter = 599
nu = 0.127432
obj = -2.307101, rho = -0.081438
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 285
nu = 0.110950
obj = -2.644671, rho = -0.052920
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.097191
obj = -3.072126, rho = -0.050854
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.089492
obj = -3.596073, rho = 0.113395
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.085482
obj = -4.170041, rho = 0.233801
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.077983
obj = -4.777340, rho = 0.243075
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
...*.*
optimization finished, #iter = 428
nu = 0.072561
obj = -5.409271, rho = 0.188216
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.063731
obj = -6.094749, rho = 0.149792
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.057126
obj = -6.863807, rho = 0.114301
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.052809
obj = -7.648028, rho = 0.200518
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.046739
obj = -8.308022, rho = 0.339963
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.040777
obj = -8.942477, rho = 0.510778
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.034843
obj = -9.484105, rho = 0.629312
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.030147
obj = -9.940050, rho = 0.753909
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.025491
obj = -10.004860, rho = 0.784270
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.020005
obj = -10.004860, rho = 0.784270
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.199890
obj = -1.405558, rho = -0.306731
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.179738
obj = -1.614191, rho = -0.330586
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.159160
obj = -1.866153, rho = -0.351764
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.143964
obj = -2.172383, rho = -0.363285
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.133785
obj = -2.515471, rho = -0.433679
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.119794
obj = -2.910094, rho = -0.437701
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.109292
obj = -3.386217, rho = -0.591402
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.100925
obj = -3.920940, rho = -0.841334
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.092859
obj = -4.515095, rho = -1.152685
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.083025
obj = -5.170390, rho = -1.345856
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.074565
obj = -5.978890, rho = -1.458966
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*...*
optimization finished, #iter = 427
nu = 0.066760
obj = -6.888493, rho = -1.587029
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
......*.*
optimization finished, #iter = 705
nu = 0.058637
obj = -8.033834, rho = -1.586981
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.......*
optimization finished, #iter = 912
nu = 0.052646
obj = -9.479478, rho = -1.636097
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
......*..*
optimization finished, #iter = 815
nu = 0.047451
obj = -11.306454, rho = -1.635662
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
......*......................*
optimization finished, #iter = 2856
nu = 0.043621
obj = -13.616936, rho = -1.650928
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..........*..............................................*
optimization finished, #iter = 5606
nu = 0.040302
obj = -16.550219, rho = -1.650934
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.......*.......................................................*
optimization finished, #iter = 6266
nu = 0.037765
obj = -20.286335, rho = -1.653969
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.......*.........................*
optimization finished, #iter = 3242
nu = 0.035753
obj = -25.029609, rho = -1.663146
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.............*.........*
optimization finished, #iter = 2269
nu = 0.034769
obj = -31.001399, rho = -1.696462
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.168087
obj = -1.071583, rho = -0.062943
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.147514
obj = -1.186300, rho = -0.088427
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.126529
obj = -1.315557, rho = -0.096473
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.110298
obj = -1.460367, rho = -0.076942
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.096457
obj = -1.626184, rho = -0.023510
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.082859
obj = -1.810308, rho = 0.002190
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.076567
obj = -2.003575, rho = 0.180524
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.067610
obj = -2.168030, rho = 0.339320
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.058756
obj = -2.312754, rho = 0.454883
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.049203
obj = -2.436026, rho = 0.481228
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.042860
obj = -2.530397, rho = 0.495392
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.035574
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.027917
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.021908
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.017193
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.013492
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.010588
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.008309
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.006521
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.005117
obj = -2.558451, rho = 0.521896
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.170313
obj = -1.112939, rho = -0.228423
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.155229
obj = -1.233809, rho = -0.384782
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.133615
obj = -1.360353, rho = -0.405505
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.116238
obj = -1.499356, rho = -0.419253
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.102110
obj = -1.642259, rho = -0.386092
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.089696
obj = -1.782242, rho = -0.272713
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.078709
obj = -1.902184, rho = -0.145175
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.066479
obj = -1.982153, rho = -0.087557
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.053567
obj = -2.074147, rho = -0.077232
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.047318
obj = -2.144796, rho = -0.093693
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.038094
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.029895
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.023460
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.018411
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.014448
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.011338
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.008898
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.006983
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.005480
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.004300
obj = -2.150303, rho = -0.076820
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.172628
obj = -1.140434, rho = -0.327428
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.149787
obj = -1.287790, rho = -0.326049
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.134422
obj = -1.460809, rho = -0.314981
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*..*
optimization finished, #iter = 246
nu = 0.119709
obj = -1.644621, rho = -0.318841
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*....*
optimization finished, #iter = 474
nu = 0.104858
obj = -1.857883, rho = -0.310942
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.093789
obj = -2.108044, rho = -0.278648
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.089647
obj = -2.346532, rho = -0.116316
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 147
nu = 0.080738
obj = -2.513906, rho = -0.037715
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.068237
obj = -2.662719, rho = -0.016694
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.058139
obj = -2.782222, rho = -0.020336
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.048070
obj = -2.873269, rho = -0.109328
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.040489
obj = -2.920405, rho = -0.182979
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.031867
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.025008
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.019625
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.015401
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.012086
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.009485
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.007443
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.005841
obj = -2.920474, rho = -0.185184
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.166048
obj = -1.115797, rho = -0.216241
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.144779
obj = -1.268919, rho = -0.216764
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.129291
obj = -1.450120, rho = -0.293762
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.117720
obj = -1.653815, rho = -0.362855
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.106930
obj = -1.864739, rho = -0.264033
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.094118
obj = -2.101548, rho = -0.172169
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.085626
obj = -2.353175, rho = -0.167204
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.075065
obj = -2.619826, rho = -0.360602
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.063725
obj = -2.929341, rho = -0.360439
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.056892
obj = -3.298780, rho = -0.411324
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.051097
obj = -3.673039, rho = -0.496084
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.045826
obj = -4.062770, rho = -0.642021
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.042041
obj = -4.377302, rho = -0.853345
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.037506
obj = -4.564142, rho = -0.605754
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.030760
obj = -4.578648, rho = -0.512128
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.024140
obj = -4.578648, rho = -0.512128
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.018944
obj = -4.578648, rho = -0.512128
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.014866
obj = -4.578648, rho = -0.512128
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.011666
obj = -4.578648, rho = -0.512128
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.009155
obj = -4.578648, rho = -0.512128
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.186359
obj = -1.269506, rho = 0.128196
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.164166
obj = -1.447875, rho = 0.129387
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.147724
obj = -1.657113, rho = 0.081156
nSV = 17, nBSV = 13
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.138512
obj = -1.873317, rho = 0.170551
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.123116
obj = -2.091150, rho = 0.201992
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.109666
obj = -2.318741, rho = 0.170866
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.093130
obj = -2.569492, rho = 0.193271
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.080896
obj = -2.867608, rho = 0.204771
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.071926
obj = -3.190607, rho = 0.115467
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 363
nu = 0.064131
obj = -3.518987, rho = 0.042279
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
...*.*
optimization finished, #iter = 440
nu = 0.054615
obj = -3.863515, rho = 0.009187
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*..*
optimization finished, #iter = 306
nu = 0.045835
obj = -4.291517, rho = 0.009044
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.040529
obj = -4.819956, rho = 0.164619
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 264
nu = 0.036893
obj = -5.323945, rho = 0.460534
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
.*.*
optimization finished, #iter = 262
nu = 0.032953
obj = -5.793361, rho = 0.696866
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 93.9% (939/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.029975
obj = -6.138931, rho = 0.988930
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 93% (930/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.025638
obj = -6.194510, rho = 1.129584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.020120
obj = -6.194510, rho = 1.129584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.015789
obj = -6.194510, rho = 1.129584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.012391
obj = -6.194510, rho = 1.129584
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 92.6% (926/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.168016
obj = -1.080097, rho = -0.230875
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.150120
obj = -1.199504, rho = -0.202610
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.133904
obj = -1.315317, rho = -0.340959
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.114695
obj = -1.431185, rho = -0.468113
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.099846
obj = -1.555771, rho = -0.524908
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.090477
obj = -1.649146, rho = -0.612874
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*...*
optimization finished, #iter = 431
nu = 0.074837
obj = -1.704041, rho = -0.656561
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 329
nu = 0.060863
obj = -1.749536, rho = -0.630792
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*..*
optimization finished, #iter = 383
nu = 0.048762
obj = -1.793763, rho = -0.631220
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*...*
optimization finished, #iter = 638
nu = 0.040865
obj = -1.823599, rho = -0.615345
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.032358
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.025393
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.019927
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.015638
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.012272
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.009631
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.007558
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.005931
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.004654
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
......*.*..*
optimization finished, #iter = 948
nu = 0.003653
obj = -1.826474, rho = -0.603547
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.177603
obj = -1.256368, rho = -0.114467
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.158749
obj = -1.448877, rho = -0.031620
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.142924
obj = -1.683407, rho = -0.054470
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.132861
obj = -1.950300, rho = -0.067556
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 167
nu = 0.120087
obj = -2.242702, rho = -0.094082
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.108223
obj = -2.596578, rho = -0.040846
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.098230
obj = -2.990491, rho = -0.005561
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.088102
obj = -3.468636, rho = 0.021736
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.080945
obj = -4.010501, rho = 0.142685
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.075315
obj = -4.621411, rho = 0.209361
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.071518
obj = -5.229152, rho = 0.184479
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.066650
obj = -5.767566, rho = -0.044897
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.058051
obj = -6.197688, rho = -0.238245
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.050340
obj = -6.599381, rho = -0.408315
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.044884
obj = -6.834701, rho = -0.619242
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.036065
obj = -6.840864, rho = -0.667953
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.028303
obj = -6.840864, rho = -0.667953
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.022211
obj = -6.840864, rho = -0.667953
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.017430
obj = -6.840864, rho = -0.667953
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.013678
obj = -6.840864, rho = -0.667953
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.202110
obj = -1.322441, rho = 0.007551
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.178226
obj = -1.475029, rho = 0.103675
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.153759
obj = -1.657862, rho = 0.088217
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.135432
obj = -1.872026, rho = 0.030769
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.121397
obj = -2.112162, rho = -0.043110
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.109178
obj = -2.362448, rho = -0.160570
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.099319
obj = -2.611219, rho = -0.240873
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.091680
obj = -2.816641, rho = -0.256428
nSV = 10, nBSV = 6
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.078600
obj = -2.929681, rho = -0.361229
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.065406
obj = -3.005620, rho = -0.439044
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.................*
optimization finished, #iter = 1845
nu = 0.052436
obj = -3.045941, rho = -0.461647
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.041972
obj = -3.087699, rho = -0.463714
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.033755
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.026489
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.020788
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.016313
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.012802
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.010047
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.007884
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.006187
obj = -3.093676, rho = -0.466973
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.211223
obj = -1.357732, rho = -0.178561
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.182616
obj = -1.517552, rho = -0.162079
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.164871
obj = -1.695153, rho = -0.109273
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.145570
obj = -1.870056, rho = -0.036741
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.123228
obj = -2.066854, rho = -0.015599
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.109556
obj = -2.290299, rho = -0.004023
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.096799
obj = -2.504297, rho = -0.047953
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.083835
obj = -2.703193, rho = -0.073431
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 119
nu = 0.071205
obj = -2.906847, rho = -0.062974
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.059474
obj = -3.120723, rho = -0.072946
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.050959
obj = -3.369201, rho = -0.034738
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.046608
obj = -3.555029, rho = 0.096632
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.038993
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.030600
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.024014
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.018845
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.014789
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.011606
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.009108
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.007147
obj = -3.573921, rho = 0.171327
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.215938
obj = -1.417432, rho = -0.191590
nSV = 26, nBSV = 20
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.188400
obj = -1.593597, rho = -0.211845
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.173416
obj = -1.779512, rho = -0.283458
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.154653
obj = -1.955101, rho = -0.268944
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.133579
obj = -2.132635, rho = -0.219427
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.112919
obj = -2.324281, rho = -0.201129
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.094899
obj = -2.551090, rho = -0.214554
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.083696
obj = -2.819643, rho = -0.151389
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.074128
obj = -3.056453, rho = -0.063857
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.063186
obj = -3.302287, rho = -0.028400
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.056816
obj = -3.506561, rho = -0.089741
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*..*
optimization finished, #iter = 324
nu = 0.046831
obj = -3.634738, rho = -0.029757
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.038288
obj = -3.763241, rho = -0.028977
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.032050
obj = -3.877185, rho = 0.026634
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.026178
obj = -3.896180, rho = 0.043319
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.020544
obj = -3.896180, rho = 0.043319
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.016122
obj = -3.896180, rho = 0.043319
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.012652
obj = -3.896180, rho = 0.043319
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.009929
obj = -3.896180, rho = 0.043319
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 234
nu = 0.007792
obj = -3.896180, rho = 0.043319
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.193648
obj = -1.249134, rho = -0.359749
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.171357
obj = -1.386663, rho = -0.186240
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.148567
obj = -1.538852, rho = -0.272107
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.128033
obj = -1.710616, rho = -0.299699
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 157
nu = 0.110226
obj = -1.915285, rho = -0.273894
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.098964
obj = -2.148276, rho = -0.251464
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.088091
obj = -2.387782, rho = -0.197299
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.077706
obj = -2.628907, rho = -0.114477
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.068105
obj = -2.873903, rho = -0.139266
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.059223
obj = -3.118344, rho = -0.153502
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.053138
obj = -3.335881, rho = -0.187476
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.046437
obj = -3.425597, rho = -0.239707
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.037437
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.029379
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.023056
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.018093
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.014199
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.011143
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.008744
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.006862
obj = -3.431729, rho = -0.267043
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.186939
obj = -1.259886, rho = -0.074456
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.165216
obj = -1.432660, rho = -0.009711
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.145185
obj = -1.635803, rho = -0.046563
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.130692
obj = -1.867875, rho = -0.214827
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.115253
obj = -2.142761, rho = -0.303264
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.105522
obj = -2.468239, rho = -0.356592
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.099209
obj = -2.806977, rho = -0.275931
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.085839
obj = -3.171859, rho = -0.240583
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.078705
obj = -3.584747, rho = -0.286389
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.070066
obj = -4.010850, rho = -0.352626
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 193
nu = 0.062786
obj = -4.456000, rho = -0.489688
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.059680
obj = -4.816820, rho = -0.683870
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.050016
obj = -5.055303, rho = -0.764849
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.041486
obj = -5.262582, rho = -0.841161
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.034239
obj = -5.471859, rho = -0.916054
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.029446
obj = -5.584967, rho = -1.031930
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.023108
obj = -5.584967, rho = -1.031930
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.018135
obj = -5.584967, rho = -1.031930
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.014231
obj = -5.584967, rho = -1.031930
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.011168
obj = -5.584967, rho = -1.031930
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.148412
obj = -0.925070, rho = -0.316719
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 33
nu = 0.128442
obj = -1.016976, rho = -0.329424
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.113674
obj = -1.112743, rho = -0.295715
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.096730
obj = -1.211262, rho = -0.267257
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.083818
obj = -1.316957, rho = -0.167882
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.070283
obj = -1.423038, rho = -0.134319
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.059013
obj = -1.551514, rho = -0.190761
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.049990
obj = -1.703248, rho = -0.224441
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.043071
obj = -1.879936, rho = -0.262733
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.040280
obj = -2.035675, rho = -0.240643
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.035416
obj = -2.134982, rho = -0.321856
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.029878
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.023447
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.018401
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.014440
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.011332
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.008893
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.006979
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.005477
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.004298
obj = -2.148876, rho = -0.447186
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.210981
obj = -1.369957, rho = 0.308009
nSV = 29, nBSV = 18
Total nSV = 29
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.191507
obj = -1.520377, rho = 0.466032
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.164766
obj = -1.676154, rho = 0.443755
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.147604
obj = -1.831497, rho = 0.449307
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.127429
obj = -1.985202, rho = 0.375927
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.111322
obj = -2.119571, rho = 0.377524
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.094023
obj = -2.223887, rho = 0.510093
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.077805
obj = -2.320838, rho = 0.536795
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.063162
obj = -2.422010, rho = 0.575118
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.050967
obj = -2.546298, rho = 0.574870
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.041537
obj = -2.704467, rho = 0.583021
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 282
nu = 0.037233
obj = -2.838527, rho = 0.666773
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.030323
obj = -2.921535, rho = 0.716253
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.025119
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.019712
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.015469
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.012140
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.009527
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.007476
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.005867
obj = -2.933808, rho = 0.863979
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.141194
obj = -0.876863, rho = -0.156336
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.125660
obj = -0.957265, rho = -0.090296
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.106779
obj = -1.037756, rho = -0.116759
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.090280
obj = -1.123943, rho = -0.097042
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.079680
obj = -1.213398, rho = 0.008816
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.067494
obj = -1.282460, rho = 0.050275
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.056639
obj = -1.355626, rho = 0.088961
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.048487
obj = -1.404413, rho = 0.078057
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.039688
obj = -1.439887, rho = 0.082925
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.032791
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.025733
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.020194
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.015848
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.012437
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.009760
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.007659
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.006011
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.004717
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.003702
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.002905
obj = -1.452610, rho = 0.092152
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.208355
obj = -1.287260, rho = -0.216101
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 129
nu = 0.180401
obj = -1.409649, rho = -0.272488
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.160219
obj = -1.524794, rho = -0.312724
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.137470
obj = -1.633991, rho = -0.261955
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.118360
obj = -1.714764, rho = -0.166110
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.096585
obj = -1.794200, rho = -0.138276
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
....*....*
optimization finished, #iter = 835
nu = 0.080357
obj = -1.875584, rho = -0.191227
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*..*
optimization finished, #iter = 481
nu = 0.064718
obj = -1.959239, rho = -0.185387
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.053295
obj = -2.059926, rho = -0.186616
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.047248
obj = -2.120333, rho = -0.191891
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.037579
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.029490
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.023143
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.018162
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.014252
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.011185
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.008777
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.006888
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.005406
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.004242
obj = -2.121126, rho = -0.196135
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.200000
obj = -1.371018, rho = -0.206371
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99.3% (993/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.181147
obj = -1.554965, rho = -0.240449
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.161770
obj = -1.768100, rho = -0.142901
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.141045
obj = -2.008710, rho = -0.136181
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.128947
obj = -2.289366, rho = -0.027283
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.114045
obj = -2.591358, rho = 0.026233
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.106395
obj = -2.912012, rho = 0.273131
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.094454
obj = -3.227826, rho = 0.309907
nSV = 12, nBSV = 7
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.084324
obj = -3.514605, rho = 0.268069
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 305
nu = 0.073541
obj = -3.787431, rho = 0.386831
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 239
nu = 0.064627
obj = -4.034777, rho = 0.476307
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*.*
optimization finished, #iter = 434
nu = 0.053196
obj = -4.204219, rho = 0.519846
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 344
nu = 0.043454
obj = -4.397770, rho = 0.536291
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.035583
obj = -4.620787, rho = 0.493263
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.031156
obj = -4.773735, rho = 0.293242
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.025219
obj = -4.783395, rho = 0.226580
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.019791
obj = -4.783395, rho = 0.226580
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.015531
obj = -4.783395, rho = 0.226580
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.012188
obj = -4.783395, rho = 0.226580
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.009565
obj = -4.783395, rho = 0.226580
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.235607
obj = -1.624960, rho = -0.530144
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.215362
obj = -1.841946, rho = -0.581898
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.186896
obj = -2.095318, rho = -0.597779
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 264
nu = 0.162854
obj = -2.410401, rho = -0.602502
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*...*
optimization finished, #iter = 413
nu = 0.147080
obj = -2.791165, rho = -0.603733
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 97% (97/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.137392
obj = -3.228369, rho = -0.592815
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 236
nu = 0.123045
obj = -3.701211, rho = -0.563121
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 254
nu = 0.108273
obj = -4.279843, rho = -0.598529
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.098709
obj = -4.989358, rho = -0.639360
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.094753
obj = -5.742893, rho = -0.697238
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.087187
obj = -6.486722, rho = -0.576029
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.077786
obj = -7.284679, rho = -0.550317
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 290
nu = 0.070463
obj = -8.095838, rho = -0.511181
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.*
optimization finished, #iter = 345
nu = 0.060155
obj = -8.961108, rho = -0.478805
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
...*.*..*
optimization finished, #iter = 650
nu = 0.053164
obj = -9.952357, rho = -0.505096
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
......*..*
optimization finished, #iter = 898
nu = 0.045232
obj = -11.049056, rho = -0.483030
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
......*.*
optimization finished, #iter = 775
nu = 0.038470
obj = -12.402549, rho = -0.487194
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 362
nu = 0.035438
obj = -13.936328, rho = -0.519427
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*..*
optimization finished, #iter = 301
nu = 0.033757
obj = -15.182378, rho = -0.561925
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 309
nu = 0.031186
obj = -15.594591, rho = -0.652243
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.181459
obj = -1.222253, rho = -0.474143
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.161702
obj = -1.381445, rho = -0.538160
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.143613
obj = -1.567002, rho = -0.464238
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.133921
obj = -1.754033, rho = -0.351490
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.120842
obj = -1.919705, rho = -0.321535
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 312
nu = 0.102901
obj = -2.079907, rho = -0.310449
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.088722
obj = -2.257420, rho = -0.352257
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.078718
obj = -2.413033, rho = -0.469289
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 279
nu = 0.068624
obj = -2.512548, rho = -0.435839
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 285
nu = 0.056126
obj = -2.571836, rho = -0.424197
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.046109
obj = -2.602318, rho = -0.491040
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.036190
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.028400
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.022287
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.017490
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.013726
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.010771
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.008453
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.006634
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.005206
obj = -2.602318, rho = -0.491215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.161425
obj = -0.983938, rho = -0.132219
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.142893
obj = -1.063267, rho = -0.204038
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.121844
obj = -1.137066, rho = -0.215557
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.101614
obj = -1.216052, rho = -0.235896
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 477
nu = 0.083415
obj = -1.305232, rho = -0.224870
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 420
nu = 0.070455
obj = -1.413229, rho = -0.227775
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.060405
obj = -1.522729, rho = -0.209945
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.052089
obj = -1.633734, rho = -0.213095
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.043568
obj = -1.737975, rho = -0.232731
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.039353
obj = -1.820679, rho = -0.068350
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.032568
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.025558
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.020057
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.015740
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.012352
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.009693
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.007607
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.005970
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.004685
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.003676
obj = -1.838209, rho = 0.084429
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.215565
obj = -1.403959, rho = -0.410991
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.186643
obj = -1.575022, rho = -0.376403
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.163509
obj = -1.774112, rho = -0.416153
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.145090
obj = -2.004757, rho = -0.509876
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.128815
obj = -2.261771, rho = -0.554242
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.113228
obj = -2.553626, rho = -0.619394
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 117
nu = 0.102399
obj = -2.880866, rho = -0.696756
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.089663
obj = -3.227410, rho = -0.745855
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.083510
obj = -3.608517, rho = -0.709644
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.075057
obj = -3.913583, rho = -0.870338
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.062599
obj = -4.219825, rho = -0.880519
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.051762
obj = -4.595765, rho = -0.883013
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.045919
obj = -5.016216, rho = -0.953026
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.039541
obj = -5.400365, rho = -1.002455
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.034366
obj = -5.751774, rho = -1.058992
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 248
nu = 0.031048
obj = -5.962053, rho = -1.176643
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*...*
optimization finished, #iter = 505
nu = 0.024676
obj = -5.963283, rho = -1.190914
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*...*
optimization finished, #iter = 505
nu = 0.019365
obj = -5.963283, rho = -1.190914
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*...*
optimization finished, #iter = 505
nu = 0.015197
obj = -5.963283, rho = -1.190914
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*...*
optimization finished, #iter = 505
nu = 0.011926
obj = -5.963283, rho = -1.190914
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.173337
obj = -1.025051, rho = -0.456906
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.151247
obj = -1.100439, rho = -0.490615
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.123896
obj = -1.172773, rho = -0.499038
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.105300
obj = -1.257789, rho = -0.546861
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*....*
optimization finished, #iter = 434
nu = 0.088329
obj = -1.336245, rho = -0.561416
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*.*
optimization finished, #iter = 307
nu = 0.073223
obj = -1.424530, rho = -0.587750
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.061572
obj = -1.522820, rho = -0.628468
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.
WARNING: using -h 0 may be faster
*
optimization finished, #iter = 131
nu = 0.053158
obj = -1.612635, rho = -0.643232
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.045284
obj = -1.675959, rho = -0.608912
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.038458
obj = -1.703495, rho = -0.636905
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.030179
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.023683
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.018586
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.014585
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.011446
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.008982
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.007049
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.005532
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.004341
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*
optimization finished, #iter = 274
nu = 0.003407
obj = -1.703495, rho = -0.637215
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.180538
obj = -1.133993, rho = -0.256029
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.159073
obj = -1.247065, rho = -0.349961
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.138758
obj = -1.358367, rho = -0.344735
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.119554
obj = -1.469993, rho = -0.336607
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.102546
obj = -1.582244, rho = -0.314532
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.085813
obj = -1.697617, rho = -0.283529
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.073251
obj = -1.816440, rho = -0.300045
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.060006
obj = -1.952439, rho = -0.308885
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.050949
obj = -2.114062, rho = -0.351230
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*..*
optimization finished, #iter = 648
nu = 0.044174
obj = -2.285598, rho = -0.316173
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.038824
obj = -2.435091, rho = -0.250314
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 284
nu = 0.033207
obj = -2.512139, rho = -0.215269
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.027830
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.021840
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.017139
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.013450
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.010555
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.008283
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.006500
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.005101
obj = -2.551066, rho = -0.249888
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.205132
obj = -1.406248, rho = -0.550817
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.181222
obj = -1.605990, rho = -0.577806
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.161074
obj = -1.842860, rho = -0.602441
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 260
nu = 0.144403
obj = -2.128939, rho = -0.671721
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*
optimization finished, #iter = 291
nu = 0.132550
obj = -2.457865, rho = -0.677460
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
....*...*
optimization finished, #iter = 787
nu = 0.118304
obj = -2.824295, rho = -0.645539
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*
optimization finished, #iter = 337
nu = 0.104477
obj = -3.277006, rho = -0.599609
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.093131
obj = -3.842987, rho = -0.660339
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.084850
obj = -4.550456, rho = -0.671091
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 247
nu = 0.080554
obj = -5.379484, rho = -0.721154
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 253
nu = 0.075327
obj = -6.318413, rho = -0.777095
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 96% (960/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.071169
obj = -7.370256, rho = -1.127421
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.066452
obj = -8.507715, rho = -1.380827
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
..*
optimization finished, #iter = 248
nu = 0.063651
obj = -9.653453, rho = -1.620205
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 93.4% (934/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.061436
obj = -10.562379, rho = -1.923322
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 92.3% (923/1000) (classification)
......*.*
optimization finished, #iter = 728
nu = 0.057076
obj = -10.890466, rho = -2.189887
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
...........*....*
optimization finished, #iter = 1504
nu = 0.045158
obj = -10.912630, rho = -2.202546
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
...........*....*
optimization finished, #iter = 1504
nu = 0.035438
obj = -10.912630, rho = -2.202546
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
...........*....*
optimization finished, #iter = 1504
nu = 0.027811
obj = -10.912630, rho = -2.202546
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
...........*....*
optimization finished, #iter = 1504
nu = 0.021825
obj = -10.912630, rho = -2.202546
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 91.6% (916/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.169179
obj = -1.135454, rho = -0.094176
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.149415
obj = -1.291051, rho = -0.080776
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.134726
obj = -1.458205, rho = -0.043072
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.118552
obj = -1.647829, rho = -0.030388
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.107768
obj = -1.862091, rho = -0.065550
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.096854
obj = -2.079165, rho = -0.160845
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.083634
obj = -2.315228, rho = -0.190435
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.076400
obj = -2.565726, rho = -0.142575
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.067979
obj = -2.777961, rho = -0.043532
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..**..*
optimization finished, #iter = 417
nu = 0.058868
obj = -2.962829, rho = 0.075678
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.*
optimization finished, #iter = 411
nu = 0.049550
obj = -3.133710, rho = -0.004833
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
........*.*
optimization finished, #iter = 929
nu = 0.041974
obj = -3.281613, rho = -0.013701
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*....*.......................*
optimization finished, #iter = 3104
nu = 0.034338
obj = -3.394639, rho = -0.002458
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.029618
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.023243
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.018240
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.014314
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.011233
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.008815
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*...*
optimization finished, #iter = 516
nu = 0.006918
obj = -3.458659, rho = 0.074091
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.214766
obj = -1.546706, rho = -0.062186
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.192231
obj = -1.799506, rho = -0.004686
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.177059
obj = -2.094501, rho = 0.098737
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.165383
obj = -2.425488, rho = 0.067891
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 36
nu = 0.155706
obj = -2.775308, rho = 0.070490
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.145855
obj = -3.109565, rho = -0.064059
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 136
nu = 0.130714
obj = -3.414755, rho = 0.025998
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 251
nu = 0.113826
obj = -3.706393, rho = 0.055083
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.094433
obj = -4.035052, rho = 0.080401
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 319
nu = 0.084414
obj = -4.358739, rho = 0.174950
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*..*.*
optimization finished, #iter = 398
nu = 0.072278
obj = -4.651639, rho = 0.162861
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*
optimization finished, #iter = 265
nu = 0.062441
obj = -4.877298, rho = 0.083978
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*
optimization finished, #iter = 294
nu = 0.053476
obj = -5.020111, rho = 0.002386
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.043023
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.033763
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.026496
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.020793
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.016317
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.012805
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 334
nu = 0.010049
obj = -5.024722, rho = -0.016681
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.194066
obj = -1.243836, rho = -0.023061
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.169476
obj = -1.388215, rho = -0.003446
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.150057
obj = -1.539084, rho = 0.023722
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.132296
obj = -1.696528, rho = -0.003070
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 295
nu = 0.114221
obj = -1.860077, rho = -0.023517
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*.........*
optimization finished, #iter = 1129
nu = 0.096798
obj = -2.044474, rho = -0.017830
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*...*
optimization finished, #iter = 307
nu = 0.081968
obj = -2.272062, rho = -0.008615
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.071132
obj = -2.547628, rho = 0.046047
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 212
nu = 0.061552
obj = -2.864558, rho = 0.093924
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.055277
obj = -3.238389, rho = 0.024039
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.048781
obj = -3.654670, rho = -0.015733
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.045493
obj = -4.074032, rho = -0.006862
nSV = 8, nBSV = 2
Total nSV = 8
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.043449
obj = -4.378349, rho = 0.000840
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.037949
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.029781
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.023371
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.018341
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.014393
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.011295
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.008864
obj = -4.432716, rho = 0.009341
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 168
nu = 0.194578
obj = -1.405071, rho = -0.204275
nSV = 27, nBSV = 17
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.176407
obj = -1.633958, rho = -0.230014
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.156605
obj = -1.911487, rho = -0.242587
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.145435
obj = -2.247499, rho = -0.333274
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.136499
obj = -2.629353, rho = -0.426293
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.127498
obj = -3.044182, rho = -0.586993
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.119890
obj = -3.498104, rho = -0.725832
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 178
nu = 0.109048
obj = -3.954565, rho = -0.840134
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.096123
obj = -4.465192, rho = -0.928762
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.084855
obj = -5.043807, rho = -1.009367
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.078778
obj = -5.653183, rho = -1.210828
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 281
nu = 0.069466
obj = -6.216833, rho = -1.345219
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.061119
obj = -6.790022, rho = -1.633419
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.052501
obj = -7.369858, rho = -1.842523
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.044435
obj = -8.021563, rho = -1.868465
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
...*.............*
optimization finished, #iter = 1604
nu = 0.038796
obj = -8.662611, rho = -2.047718
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*...*
optimization finished, #iter = 427
nu = 0.035126
obj = -9.191977, rho = -2.175004
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
...*.*
optimization finished, #iter = 459
nu = 0.030141
obj = -9.283181, rho = -2.335062
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
...*.*
optimization finished, #iter = 459
nu = 0.023654
obj = -9.283181, rho = -2.335062
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
...*.*
optimization finished, #iter = 459
nu = 0.018563
obj = -9.283181, rho = -2.335062
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.191364
obj = -1.296574, rho = -0.148516
nSV = 23, nBSV = 17
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.172930
obj = -1.464625, rho = -0.201150
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.151881
obj = -1.653767, rho = -0.221482
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.136111
obj = -1.875439, rho = -0.262821
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.120432
obj = -2.108509, rho = -0.198983
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.106269
obj = -2.381770, rho = -0.280656
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.095681
obj = -2.669649, rho = -0.334653
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.084373
obj = -2.991539, rho = -0.347428
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.074306
obj = -3.343794, rho = -0.373304
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 226
nu = 0.065236
obj = -3.724356, rho = -0.403231
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.057051
obj = -4.157512, rho = -0.437348
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 376
nu = 0.050354
obj = -4.610207, rho = -0.551402
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
..*.*
optimization finished, #iter = 388
nu = 0.042530
obj = -5.151449, rho = -0.551876
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*..*
optimization finished, #iter = 406
nu = 0.036391
obj = -5.837148, rho = -0.552368
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
..*..*
optimization finished, #iter = 415
nu = 0.031573
obj = -6.704432, rho = -0.552908
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.........*
optimization finished, #iter = 1195
nu = 0.029607
obj = -7.721143, rho = -0.780581
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.028358
obj = -8.709257, rho = -0.978206
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.027765
obj = -9.402044, rho = -1.231261
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
...*..*.*
optimization finished, #iter = 599
nu = 0.024218
obj = -9.505457, rho = -1.373246
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
...*..*.*
optimization finished, #iter = 599
nu = 0.019005
obj = -9.505457, rho = -1.373246
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.155291
obj = -0.989539, rho = -0.359434
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.134059
obj = -1.100089, rho = -0.359337
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.116649
obj = -1.228570, rho = -0.412418
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.101661
obj = -1.372950, rho = -0.432324
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 239
nu = 0.087023
obj = -1.546396, rho = -0.446069
nSV = 17, nBSV = 6
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.079340
obj = -1.750053, rho = -0.521592
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.076718
obj = -1.923875, rho = -0.617640
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.068779
obj = -2.020403, rho = -0.663352
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*..*
optimization finished, #iter = 219
nu = 0.055347
obj = -2.093824, rho = -0.666801
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 304
nu = 0.047280
obj = -2.164401, rho = -0.726459
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.037937
obj = -2.197069, rho = -0.714129
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.030643
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.024048
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.018872
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.014810
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.011622
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.009121
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.007157
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.005617
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.004408
obj = -2.204161, rho = -0.590521
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 180
nu = 0.173832
obj = -1.114326, rho = 0.009750
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 276
nu = 0.149122
obj = -1.246082, rho = 0.030755
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.131055
obj = -1.398109, rho = 0.073355
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 118
nu = 0.113602
obj = -1.576062, rho = 0.104077
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.100351
obj = -1.789191, rho = 0.047264
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.093257
obj = -2.000700, rho = -0.032931
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.082466
obj = -2.218938, rho = 0.003223
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.072332
obj = -2.433589, rho = 0.046665
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.063858
obj = -2.658761, rho = 0.118840
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.057533
obj = -2.838162, rho = 0.220605
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.048486
obj = -2.954222, rho = 0.273238
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
...*....*
optimization finished, #iter = 790
nu = 0.039853
obj = -3.054053, rho = 0.293384
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.033893
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.026598
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.020873
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.016380
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.012855
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.010088
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.007916
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*.*
optimization finished, #iter = 305
nu = 0.006212
obj = -3.105660, rho = 0.353115
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.151696
obj = -0.925651, rho = 0.299279
nSV = 17, nBSV = 12
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.132874
obj = -1.008397, rho = 0.301426
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.113678
obj = -1.087099, rho = 0.316357
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.094701
obj = -1.171762, rho = 0.321516
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.078756
obj = -1.275612, rho = 0.306823
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.067487
obj = -1.399809, rho = 0.254598
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.060200
obj = -1.526785, rho = 0.156375
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.051886
obj = -1.634831, rho = 0.097355
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*......*
optimization finished, #iter = 676
nu = 0.042771
obj = -1.751671, rho = 0.072484
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 215
nu = 0.035771
obj = -1.891651, rho = 0.070229
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.030335
obj = -2.046997, rho = 0.125630
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.028115
obj = -2.178031, rho = 0.365934
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.023953
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.018797
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.014751
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.011576
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.009085
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.007129
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.005595
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 35
nu = 0.004391
obj = -2.195283, rho = 0.489877
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.188779
obj = -1.300144, rho = -0.328774
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.167463
obj = -1.486230, rho = -0.323677
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.147948
obj = -1.712288, rho = -0.336066
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.134681
obj = -1.980251, rho = -0.316742
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.122464
obj = -2.279549, rho = -0.319888
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.109745
obj = -2.635897, rho = -0.313133
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.100473
obj = -3.040469, rho = -0.354330
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.095515
obj = -3.471462, rho = -0.306749
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.086145
obj = -3.875132, rho = -0.230590
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 168
nu = 0.073542
obj = -4.352162, rho = -0.196987
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.065208
obj = -4.932560, rho = -0.176534
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.059254
obj = -5.543149, rho = -0.165153
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.054095
obj = -6.152308, rho = -0.112339
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.048314
obj = -6.709355, rho = 0.041970
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.043778
obj = -7.135308, rho = 0.238798
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 360
nu = 0.038220
obj = -7.247269, rho = 0.404141
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*.*
optimization finished, #iter = 360
nu = 0.029994
obj = -7.247269, rho = 0.404141
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*.*
optimization finished, #iter = 360
nu = 0.023538
obj = -7.247269, rho = 0.404141
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*.*
optimization finished, #iter = 360
nu = 0.018472
obj = -7.247269, rho = 0.404141
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*.*
optimization finished, #iter = 360
nu = 0.014496
obj = -7.247269, rho = 0.404141
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 30
nu = 0.205885
obj = -1.435127, rho = -0.055627
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.192408
obj = -1.631140, rho = 0.021530
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.169900
obj = -1.842930, rho = -0.015477
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.151278
obj = -2.076756, rho = -0.003628
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.135238
obj = -2.334087, rho = -0.024070
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.118686
obj = -2.613640, rho = -0.093503
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 171
nu = 0.104860
obj = -2.929094, rho = -0.092664
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.091613
obj = -3.291133, rho = -0.020898
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.078640
obj = -3.725042, rho = -0.022869
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.070688
obj = -4.239278, rho = 0.052513
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.064779
obj = -4.781565, rho = 0.130128
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.058864
obj = -5.331810, rho = 0.013439
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.052619
obj = -5.833899, rho = 0.046253
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.043780
obj = -6.384172, rho = 0.061814
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*
optimization finished, #iter = 173
nu = 0.039000
obj = -7.026032, rho = 0.027552
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.5% (955/1000) (classification)
..*.*
optimization finished, #iter = 333
nu = 0.035179
obj = -7.522649, rho = 0.031480
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.4% (954/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.031929
obj = -7.717654, rho = 0.097829
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.025057
obj = -7.717654, rho = 0.097829
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.019663
obj = -7.717654, rho = 0.097829
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.015431
obj = -7.717654, rho = 0.097829
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.181354
obj = -1.166700, rho = -0.124047
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.161403
obj = -1.290628, rho = -0.137198
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.141226
obj = -1.418898, rho = -0.106586
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.121883
obj = -1.558728, rho = -0.088575
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.107637
obj = -1.693421, rho = -0.023453
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.090587
obj = -1.833815, rho = -0.003715
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.075329
obj = -2.004532, rho = -0.011613
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 180
nu = 0.065343
obj = -2.208607, rho = 0.017373
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.060000
obj = -2.398076, rho = 0.146853
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.050795
obj = -2.522846, rho = 0.243057
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 319
nu = 0.041226
obj = -2.671327, rho = 0.250241
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.034514
obj = -2.845044, rho = 0.180174
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.030559
obj = -2.988873, rho = 0.167978
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.025811
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.020255
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.015895
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.012474
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.009789
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.007682
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.006029
obj = -3.014907, rho = 0.172938
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.177734
obj = -1.106746, rho = 0.194286
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.154591
obj = -1.211641, rho = 0.152600
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.135446
obj = -1.320734, rho = 0.095851
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.115336
obj = -1.433366, rho = 0.005345
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.100925
obj = -1.547170, rho = -0.008486
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.084982
obj = -1.651979, rho = 0.025255
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.072515
obj = -1.754570, rho = 0.071739
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.064067
obj = -1.826425, rho = 0.213281
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.052824
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.041454
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.032531
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.025529
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.020034
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.015722
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.012338
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.009682
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.007598
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.005963
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.004679
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.003672
obj = -1.836383, rho = 0.164774
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*..*
optimization finished, #iter = 241
nu = 0.210492
obj = -1.413852, rho = -0.080586
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.184647
obj = -1.607284, rho = -0.038659
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.166583
obj = -1.831185, rho = -0.067270
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.151249
obj = -2.067979, rho = -0.116568
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 241
nu = 0.137672
obj = -2.306418, rho = -0.122618
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 160
nu = 0.120091
obj = -2.558606, rho = -0.119275
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.108163
obj = -2.799009, rho = -0.035762
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.094253
obj = -3.042385, rho = 0.025264
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 164
nu = 0.080676
obj = -3.277783, rho = 0.083141
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.067761
obj = -3.512649, rho = 0.136205
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.......*
optimization finished, #iter = 955
nu = 0.056191
obj = -3.786498, rho = 0.144074
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.050214
obj = -4.061949, rho = 0.284737
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 290
nu = 0.041965
obj = -4.264260, rho = 0.414766
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*.*
optimization finished, #iter = 368
nu = 0.034744
obj = -4.466985, rho = 0.504301
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
..*..*
optimization finished, #iter = 431
nu = 0.029514
obj = -4.636793, rho = 0.474216
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.....*.*
optimization finished, #iter = 689
nu = 0.024787
obj = -4.701177, rho = 0.478275
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.....*.*
optimization finished, #iter = 689
nu = 0.019452
obj = -4.701177, rho = 0.478275
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.....*.*
optimization finished, #iter = 689
nu = 0.015265
obj = -4.701177, rho = 0.478275
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.....*.*
optimization finished, #iter = 689
nu = 0.011980
obj = -4.701177, rho = 0.478275
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.....*.*
optimization finished, #iter = 689
nu = 0.009401
obj = -4.701177, rho = 0.478275
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.163759
obj = -1.045655, rho = -0.033934
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.144385
obj = -1.155464, rho = -0.066988
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.125011
obj = -1.277791, rho = -0.114179
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.105377
obj = -1.421055, rho = -0.079331
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.096604
obj = -1.575700, rho = -0.173228
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.081826
obj = -1.732241, rho = -0.143030
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.070355
obj = -1.925729, rho = -0.091486
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.064920
obj = -2.113447, rho = 0.112511
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.058463
obj = -2.247994, rho = 0.348963
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.050320
obj = -2.309674, rho = 0.526902
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.041401
obj = -2.336554, rho = 0.611530
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.032490
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.025497
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.020009
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.015702
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.012322
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.009670
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.007589
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.005955
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.004673
obj = -2.336554, rho = 0.611290
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.155205
obj = -0.951008, rho = -0.085921
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.130919
obj = -1.043674, rho = -0.071470
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.111336
obj = -1.157212, rho = -0.062316
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.096710
obj = -1.288000, rho = 0.006306
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.083138
obj = -1.441616, rho = 0.027302
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.074725
obj = -1.617723, rho = -0.008229
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.070269
obj = -1.775310, rho = -0.002214
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.063403
obj = -1.874839, rho = 0.122190
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.052839
obj = -1.936485, rho = 0.154187
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.044171
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.034664
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.027203
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.021348
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.016753
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.013147
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.010317
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.008096
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.006354
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.004986
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.003913
obj = -1.956731, rho = 0.281297
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.188158
obj = -1.316493, rho = 0.043724
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.171521
obj = -1.508976, rho = 0.014031
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.151944
obj = -1.727226, rho = 0.010940
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.136875
obj = -1.981602, rho = -0.013689
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.122695
obj = -2.278850, rho = 0.084274
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.112041
obj = -2.623803, rho = 0.114297
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.103762
obj = -2.983070, rho = 0.026889
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.090674
obj = -3.392458, rho = 0.015923
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.081014
obj = -3.871940, rho = 0.078681
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.074562
obj = -4.401570, rho = 0.101334
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.069925
obj = -4.910367, rho = 0.102680
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 244
nu = 0.063587
obj = -5.302684, rho = 0.125319
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 364
nu = 0.052701
obj = -5.671022, rho = 0.120612
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 255
nu = 0.047948
obj = -5.971581, rho = 0.279924
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*...................................*
optimization finished, #iter = 3881
nu = 0.039871
obj = -6.143643, rho = 0.236080
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
..*.*
optimization finished, #iter = 365
nu = 0.032149
obj = -6.248066, rho = 0.231604
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
..*.*
optimization finished, #iter = 347
nu = 0.025878
obj = -6.253812, rho = 0.242205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 347
nu = 0.020308
obj = -6.253812, rho = 0.242205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 347
nu = 0.015937
obj = -6.253812, rho = 0.242205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 347
nu = 0.012507
obj = -6.253812, rho = 0.242205
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.212031
obj = -1.525021, rho = -0.092390
nSV = 27, nBSV = 19
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.195728
obj = -1.760803, rho = -0.014454
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.174616
obj = -2.032446, rho = 0.071277
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 133
nu = 0.158665
obj = -2.354026, rho = 0.105924
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.142522
obj = -2.732718, rho = 0.122304
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 164
nu = 0.128878
obj = -3.186230, rho = 0.089144
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.121935
obj = -3.702878, rho = -0.035282
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.110916
obj = -4.257398, rho = -0.076138
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.102424
obj = -4.874472, rho = -0.253025
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.091941
obj = -5.542051, rho = -0.127774
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.082778
obj = -6.303389, rho = 0.115724
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.075759
obj = -7.087739, rho = 0.352624
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.068698
obj = -7.886120, rho = 0.430457
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*.*
optimization finished, #iter = 312
nu = 0.058435
obj = -8.711384, rho = 0.472444
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 289
nu = 0.050894
obj = -9.681282, rho = 0.564444
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 282
nu = 0.044511
obj = -10.757941, rho = 0.624764
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 258
nu = 0.041388
obj = -11.790433, rho = 1.035857
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 396
nu = 0.038015
obj = -12.361450, rho = 1.177750
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*.*
optimization finished, #iter = 419
nu = 0.031670
obj = -12.430334, rho = 1.236265
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*.*
optimization finished, #iter = 419
nu = 0.024853
obj = -12.430334, rho = 1.236265
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.220679
obj = -1.477566, rho = -0.151240
nSV = 26, nBSV = 18
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.196079
obj = -1.667769, rho = -0.164253
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.171958
obj = -1.891137, rho = -0.137675
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.153829
obj = -2.145707, rho = -0.142401
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 77
nu = 0.139176
obj = -2.423901, rho = -0.193993
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.123663
obj = -2.723708, rho = -0.134675
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 267
nu = 0.110214
obj = -3.044441, rho = -0.088225
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.096488
obj = -3.396855, rho = -0.116668
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 189
nu = 0.084886
obj = -3.796027, rho = -0.068831
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.079245
obj = -4.187875, rho = 0.097267
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*
optimization finished, #iter = 305
nu = 0.070211
obj = -4.492584, rho = 0.091061
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 296
nu = 0.063313
obj = -4.647728, rho = -0.046066
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.050983
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.040010
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.031398
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.024640
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.019336
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.015174
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.011908
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*...*
optimization finished, #iter = 517
nu = 0.009345
obj = -4.673003, rho = -0.076106
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 44
nu = 0.174501
obj = -1.115810, rho = -0.223153
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.150351
obj = -1.244121, rho = -0.237299
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.135200
obj = -1.384613, rho = -0.205026
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.118020
obj = -1.528613, rho = -0.173181
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.101349
obj = -1.684887, rho = -0.163061
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.087282
obj = -1.866745, rho = -0.123896
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.076990
obj = -2.068578, rho = -0.081841
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.066102
obj = -2.290519, rho = -0.122198
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.057969
obj = -2.534209, rho = -0.200775
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 157
nu = 0.051142
obj = -2.789210, rho = -0.286796
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.049185
obj = -2.972339, rho = -0.346406
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
...*...*
optimization finished, #iter = 604
nu = 0.041495
obj = -3.024160, rho = -0.361290
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.033046
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.025933
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.020351
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.015971
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.012533
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.009836
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.007719
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.006057
obj = -3.028214, rho = -0.378803
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.181842
obj = -1.184926, rho = 0.099695
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.161468
obj = -1.325348, rho = 0.117319
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.140458
obj = -1.479822, rho = 0.182760
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.125620
obj = -1.650791, rho = 0.182197
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.113360
obj = -1.811975, rho = 0.078662
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.097384
obj = -1.964801, rho = 0.109819
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.083227
obj = -2.127386, rho = 0.144229
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 379
nu = 0.074435
obj = -2.264803, rho = 0.297067
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*
optimization finished, #iter = 231
nu = 0.062013
obj = -2.381063, rho = 0.357268
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 126
nu = 0.052546
obj = -2.466602, rho = 0.445361
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.043727
obj = -2.511927, rho = 0.490570
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.034977
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.027449
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.021541
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.016904
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.013266
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.010410
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.008170
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.006411
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.005031
obj = -2.515469, rho = 0.499860
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.180516
obj = -1.275999, rho = -0.441686
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.164321
obj = -1.469804, rho = -0.421406
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.148491
obj = -1.693835, rho = -0.401707
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.136049
obj = -1.941339, rho = -0.361465
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 144
nu = 0.119222
obj = -2.224895, rho = -0.328087
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 190
nu = 0.105543
obj = -2.577917, rho = -0.318675
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.100000
obj = -2.992834, rho = -0.258160
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.090840
obj = -3.416883, rho = -0.286609
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.086555
obj = -3.851212, rho = -0.306558
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.076288
obj = -4.257182, rho = -0.544077
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.064927
obj = -4.726438, rho = -0.663601
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.055849
obj = -5.287539, rho = -0.736121
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.050274
obj = -5.925320, rho = -0.679422
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.046955
obj = -6.506869, rho = -0.389496
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 138
nu = 0.042534
obj = -6.902768, rho = -0.131741
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.036888
obj = -6.994703, rho = -0.032627
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.028949
obj = -6.994703, rho = -0.032627
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.022718
obj = -6.994703, rho = -0.032627
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.017828
obj = -6.994703, rho = -0.032627
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.013991
obj = -6.994703, rho = -0.032627
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.220797
obj = -1.499668, rho = -0.020729
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.202274
obj = -1.695928, rho = -0.148150
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.175678
obj = -1.904970, rho = -0.153728
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.152158
obj = -2.164518, rho = -0.197088
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.139988
obj = -2.461836, rho = -0.214092
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.124523
obj = -2.766808, rho = -0.218687
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.112295
obj = -3.111856, rho = -0.282930
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.100858
obj = -3.450443, rho = -0.456774
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.090100
obj = -3.781462, rho = -0.505528
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.076790
obj = -4.119199, rho = -0.640841
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.068691
obj = -4.434028, rho = -0.838440
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.060011
obj = -4.650677, rho = -0.671417
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.051488
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.040406
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.031709
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.024884
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.019528
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.015325
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.012026
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.009438
obj = -4.719757, rho = -0.403144
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.159815
obj = -1.016432, rho = 0.054847
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.136198
obj = -1.133161, rho = -0.038178
nSV = 20, nBSV = 9
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.119576
obj = -1.274283, rho = -0.056043
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.108999
obj = -1.417831, rho = -0.052132
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.095668
obj = -1.561642, rho = -0.014024
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.084800
obj = -1.695510, rho = 0.040156
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.075814
obj = -1.816316, rho = 0.052629
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 193
nu = 0.066048
obj = -1.871846, rho = 0.003550
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.052638
obj = -1.908706, rho = 0.038684
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.042218
obj = -1.946945, rho = 0.093583
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 300
nu = 0.034528
obj = -1.979231, rho = 0.191749
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.027536
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.021609
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.016958
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.013308
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.010443
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.008196
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.006432
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.005047
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 277
nu = 0.003961
obj = -1.980512, rho = 0.232619
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.187378
obj = -1.262079, rho = -0.443219
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.166132
obj = -1.433957, rho = -0.394904
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.152062
obj = -1.620995, rho = -0.443949
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.137904
obj = -1.813105, rho = -0.602107
nSV = 16, nBSV = 11
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.118846
obj = -2.014323, rho = -0.660553
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 335
nu = 0.102233
obj = -2.252619, rho = -0.667611
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*
optimization finished, #iter = 398
nu = 0.088637
obj = -2.536607, rho = -0.632298
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
...*...*
optimization finished, #iter = 670
nu = 0.081368
obj = -2.842218, rho = -0.764320
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.070479
obj = -3.161160, rho = -0.834076
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*..*
optimization finished, #iter = 382
nu = 0.062660
obj = -3.511506, rho = -0.946324
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 272
nu = 0.055734
obj = -3.872155, rho = -1.016990
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.050025
obj = -4.202664, rho = -1.095688
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.044173
obj = -4.456176, rho = -1.135651
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
...*....*
optimization finished, #iter = 704
nu = 0.038095
obj = -4.566091, rho = -1.163947
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
......*...........*
optimization finished, #iter = 1713
nu = 0.030241
obj = -4.627427, rho = -1.163585
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
...*.*
optimization finished, #iter = 471
nu = 0.023913
obj = -4.696407, rho = -1.163393
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 357
nu = 0.019572
obj = -4.729986, rho = -1.092045
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 357
nu = 0.015359
obj = -4.729986, rho = -1.092045
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 357
nu = 0.012053
obj = -4.729986, rho = -1.092045
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 357
nu = 0.009459
obj = -4.729986, rho = -1.092045
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.243575
obj = -1.564573, rho = -0.159924
nSV = 28, nBSV = 21
Total nSV = 28
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.**.*
optimization finished, #iter = 164
nu = 0.212766
obj = -1.739348, rho = -0.149716
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.185619
obj = -1.938045, rho = -0.204148
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.163282
obj = -2.154543, rho = -0.189200
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 235
nu = 0.139847
obj = -2.393214, rho = -0.185694
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.120064
obj = -2.691634, rho = -0.170078
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
..*..*
optimization finished, #iter = 419
nu = 0.103585
obj = -3.057929, rho = -0.155524
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.091656
obj = -3.507739, rho = -0.115977
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
..*
optimization finished, #iter = 259
nu = 0.086996
obj = -3.996640, rho = -0.304971
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.074859
obj = -4.522654, rho = -0.346008
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 304
nu = 0.068382
obj = -5.126624, rho = -0.584439
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.....*....*
optimization finished, #iter = 910
nu = 0.061128
obj = -5.771984, rho = -0.688247
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.....*....*
optimization finished, #iter = 937
nu = 0.052834
obj = -6.507667, rho = -0.652469
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.....*
optimization finished, #iter = 549
nu = 0.047913
obj = -7.348290, rho = -0.711412
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
......*...*
optimization finished, #iter = 964
nu = 0.044051
obj = -8.172898, rho = -0.851280
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.......*
optimization finished, #iter = 1074
nu = 0.039821
obj = -8.865625, rho = -0.979479
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
....*..*
optimization finished, #iter = 645
nu = 0.035930
obj = -9.401740, rho = -0.886626
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
........*.....*
optimization finished, #iter = 1327
nu = 0.030884
obj = -9.510756, rho = -0.820033
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
........*.....*
optimization finished, #iter = 1327
nu = 0.024236
obj = -9.510756, rho = -0.820033
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
........*.....*
optimization finished, #iter = 1327
nu = 0.019020
obj = -9.510756, rho = -0.820033
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.148315
obj = -0.937515, rho = -0.068807
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 165
nu = 0.126306
obj = -1.042628, rho = -0.042196
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.111051
obj = -1.167349, rho = -0.065886
nSV = 13, nBSV = 8
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.100879
obj = -1.288260, rho = -0.138168
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.086925
obj = -1.411086, rho = -0.203441
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.074910
obj = -1.544381, rho = -0.187613
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.064041
obj = -1.689396, rho = -0.164526
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.060188
obj = -1.815667, rho = -0.138134
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.050782
obj = -1.870945, rho = -0.024607
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.041264
obj = -1.919752, rho = 0.100415
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.034242
obj = -1.948772, rho = 0.190154
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.027104
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.021270
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.016692
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.013099
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.010280
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.008067
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.006331
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.004968
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.003899
obj = -1.949297, rho = 0.203478
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.197013
obj = -1.297848, rho = -0.427257
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.175097
obj = -1.459277, rho = -0.412719
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.153390
obj = -1.638704, rho = -0.340108
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.135452
obj = -1.835612, rho = -0.282533
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.120171
obj = -2.054525, rho = -0.154350
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.103303
obj = -2.308470, rho = -0.180697
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.089743
obj = -2.613288, rho = -0.137126
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 110
nu = 0.077833
obj = -2.990789, rho = -0.112013
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.068901
obj = -3.456995, rho = -0.051915
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.062961
obj = -4.015191, rho = 0.081258
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.061320
obj = -4.594827, rho = 0.162930
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.058464
obj = -5.085220, rho = 0.248962
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.053066
obj = -5.451434, rho = 0.093228
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.047134
obj = -5.639513, rho = 0.121560
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.037928
obj = -5.644992, rho = 0.134289
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.029764
obj = -5.644992, rho = 0.134289
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.023358
obj = -5.644992, rho = 0.134289
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.018330
obj = -5.644992, rho = 0.134289
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.014385
obj = -5.644992, rho = 0.134289
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.011289
obj = -5.644992, rho = 0.134289
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.184027
obj = -1.178668, rho = 0.190593
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.156592
obj = -1.318947, rho = 0.168543
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.144224
obj = -1.472244, rho = 0.271843
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*..*
optimization finished, #iter = 385
nu = 0.123598
obj = -1.627644, rho = 0.325998
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*...*
optimization finished, #iter = 480
nu = 0.105753
obj = -1.814024, rho = 0.330430
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.092839
obj = -2.029087, rho = 0.304962
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.083132
obj = -2.252279, rho = 0.278275
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.071769
obj = -2.499875, rho = 0.443674
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.064143
obj = -2.768518, rho = 0.410370
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.055818
obj = -3.023897, rho = 0.371599
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.049185
obj = -3.297223, rho = 0.339541
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.043520
obj = -3.514809, rho = 0.321340
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 138
nu = 0.039453
obj = -3.621542, rho = 0.259623
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.031006
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.024333
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.019095
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.014985
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.011760
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.009229
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.007242
obj = -3.621557, rho = 0.259208
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.190478
obj = -1.178587, rho = 0.015908
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 163
nu = 0.173386
obj = -1.278123, rho = -0.010953
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.150475
obj = -1.351251, rho = 0.005840
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*..........*
optimization finished, #iter = 1207
nu = 0.125142
obj = -1.406836, rho = 0.006962
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...**..*
optimization finished, #iter = 503
nu = 0.101993
obj = -1.462905, rho = 0.012074
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.....*..*
optimization finished, #iter = 705
nu = 0.082029
obj = -1.527399, rho = 0.009506
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*....*
optimization finished, #iter = 716
nu = 0.067092
obj = -1.600683, rho = 0.004381
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*...*.*
optimization finished, #iter = 539
nu = 0.055563
obj = -1.684590, rho = 0.017806
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*...........*
optimization finished, #iter = 1182
nu = 0.047875
obj = -1.748100, rho = 0.048093
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.039186
obj = -1.776288, rho = 0.072288
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.031509
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.024727
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.019405
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.015228
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.011950
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.009378
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.007360
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.005776
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.004532
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.003557
obj = -1.778468, rho = 0.090349
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.203295
obj = -1.293943, rho = -0.395186
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.179653
obj = -1.432080, rho = -0.475210
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.156414
obj = -1.580718, rho = -0.489116
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 203
nu = 0.137547
obj = -1.727494, rho = -0.556026
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.117453
obj = -1.883381, rho = -0.561845
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.105176
obj = -2.039246, rho = -0.578077
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.089151
obj = -2.157485, rho = -0.585970
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.075267
obj = -2.272718, rho = -0.556663
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.063450
obj = -2.370541, rho = -0.519840
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.053426
obj = -2.426089, rho = -0.433952
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.043125
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.033843
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.026558
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.020842
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.016356
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.012835
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.010073
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.007905
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.006203
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.004868
obj = -2.433674, rho = -0.385446
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.200253
obj = -1.373717, rho = -0.479874
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.177120
obj = -1.571469, rho = -0.488630
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.162520
obj = -1.792330, rho = -0.456981
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.146332
obj = -2.023227, rho = -0.434914
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
..*
optimization finished, #iter = 258
nu = 0.126279
obj = -2.298693, rho = -0.442833
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.109556
obj = -2.646153, rho = -0.454150
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 120
nu = 0.097057
obj = -3.085923, rho = -0.509514
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 131
nu = 0.089277
obj = -3.615275, rho = -0.586314
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.081059
obj = -4.242174, rho = -0.426266
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.073652
obj = -5.012322, rho = -0.480779
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 139
nu = 0.071368
obj = -5.895568, rho = -0.441326
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.068258
obj = -6.811658, rho = -0.484655
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.062352
obj = -7.775812, rho = -0.552776
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.058508
obj = -8.756660, rho = -0.654911
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.056470
obj = -9.556029, rho = -1.015262
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 198
nu = 0.049282
obj = -9.933609, rho = -1.364870
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.041700
obj = -10.079225, rho = -1.698435
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.032725
obj = -10.079225, rho = -1.698435
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.025681
obj = -10.079225, rho = -1.698435
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 249
nu = 0.020153
obj = -10.079225, rho = -1.698435
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.182631
obj = -1.181379, rho = -0.122051
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.159554
obj = -1.321424, rho = -0.108448
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.141625
obj = -1.474160, rho = -0.219519
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.124059
obj = -1.638807, rho = -0.198235
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.108604
obj = -1.821748, rho = -0.137648
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.098615
obj = -2.001978, rho = -0.086771
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*......*
optimization finished, #iter = 780
nu = 0.083821
obj = -2.166198, rho = -0.063766
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.070332
obj = -2.367395, rho = -0.032957
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.060482
obj = -2.596748, rho = 0.048567
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.051639
obj = -2.852389, rho = -0.028628
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.043678
obj = -3.150283, rho = -0.070405
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.037317
obj = -3.523627, rho = -0.075909
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*..*
optimization finished, #iter = 407
nu = 0.032707
obj = -3.965930, rho = -0.139402
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.030498
obj = -4.425048, rho = -0.278833
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.029142
obj = -4.751602, rho = -0.461545
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.025331
obj = -4.804874, rho = -0.575607
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.019879
obj = -4.804874, rho = -0.575607
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.015600
obj = -4.804874, rho = -0.575607
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.012242
obj = -4.804874, rho = -0.575607
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.009607
obj = -4.804874, rho = -0.575607
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.176598
obj = -1.175847, rho = -0.084562
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.160997
obj = -1.315176, rho = -0.066068
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.139625
obj = -1.469577, rho = 0.010023
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*...*
optimization finished, #iter = 344
nu = 0.123566
obj = -1.634933, rho = 0.031904
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*....*
optimization finished, #iter = 550
nu = 0.107034
obj = -1.819186, rho = -0.034995
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 137
nu = 0.092696
obj = -2.039893, rho = -0.032636
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.081145
obj = -2.286663, rho = -0.029282
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.071331
obj = -2.571522, rho = -0.038741
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.064186
obj = -2.891298, rho = -0.068297
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.056078
obj = -3.234884, rho = -0.193710
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.053537
obj = -3.555806, rho = -0.423157
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.047473
obj = -3.742483, rho = -0.632323
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.038777
obj = -3.902757, rho = -0.659876
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.032965
obj = -4.053142, rho = -0.864513
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.027382
obj = -4.076059, rho = -0.989724
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.021488
obj = -4.076059, rho = -0.989724
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.016863
obj = -4.076059, rho = -0.989724
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.013234
obj = -4.076059, rho = -0.989724
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.010385
obj = -4.076059, rho = -0.989724
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.008150
obj = -4.076059, rho = -0.989724
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.169703
obj = -1.089958, rho = -0.365678
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.149713
obj = -1.209838, rho = -0.394637
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.130979
obj = -1.336639, rho = -0.391642
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.111951
obj = -1.481066, rho = -0.387950
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 130
nu = 0.096607
obj = -1.647650, rho = -0.388219
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.087044
obj = -1.828453, rho = -0.583074
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.076147
obj = -2.008767, rho = -0.764296
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.067036
obj = -2.190460, rho = -1.008304
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.058434
obj = -2.350843, rho = -1.216228
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*.*
optimization finished, #iter = 268
nu = 0.052042
obj = -2.477002, rho = -1.533892
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.044148
obj = -2.522112, rho = -1.783883
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.035126
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.027565
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.021632
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.016976
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.013322
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.010455
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.008204
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.006439
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.005053
obj = -2.526164, rho = -1.838528
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.3% (943/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.167895
obj = -1.123663, rho = -0.072216
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 41
nu = 0.147707
obj = -1.273636, rho = -0.049848
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 99.2% (992/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.132132
obj = -1.443132, rho = -0.040115
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.117122
obj = -1.634238, rho = 0.020507
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.103987
obj = -1.855719, rho = 0.092093
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.095696
obj = -2.097281, rho = 0.248274
nSV = 11, nBSV = 6
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.088266
obj = -2.319711, rho = 0.485445
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.080027
obj = -2.501203, rho = 0.733414
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.067879
obj = -2.638878, rho = 0.778251
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 174
nu = 0.055615
obj = -2.788540, rho = 0.798199
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.046368
obj = -2.949164, rho = 0.789259
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*..*......*
optimization finished, #iter = 998
nu = 0.039006
obj = -3.103302, rho = 0.766025
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
......*.*
optimization finished, #iter = 745
nu = 0.033170
obj = -3.227809, rho = 0.712397
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 359
nu = 0.027415
obj = -3.276023, rho = 0.695247
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.022060
obj = -3.283065, rho = 0.669061
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.017312
obj = -3.283065, rho = 0.669061
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.013586
obj = -3.283065, rho = 0.669061
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.010661
obj = -3.283065, rho = 0.669061
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.008367
obj = -3.283065, rho = 0.669061
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
..*.*
optimization finished, #iter = 337
nu = 0.006566
obj = -3.283065, rho = 0.669061
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.172970
obj = -1.087608, rho = -0.169844
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.149808
obj = -1.200178, rho = -0.227517
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.130692
obj = -1.321348, rho = -0.313826
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.113041
obj = -1.457411, rho = -0.287106
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 229
nu = 0.101713
obj = -1.588542, rho = -0.203644
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.090165
obj = -1.689437, rho = -0.103687
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.074050
obj = -1.774869, rho = -0.109319
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.060828
obj = -1.870852, rho = -0.164441
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.050948
obj = -1.977098, rho = -0.235726
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*...*
optimization finished, #iter = 536
nu = 0.045406
obj = -2.042308, rho = -0.375899
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.036245
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.028444
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.022322
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.017517
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.013747
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.010788
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.008466
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.006644
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.005214
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.....*....*
optimization finished, #iter = 917
nu = 0.004092
obj = -2.046039, rho = -0.378119
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.209314
obj = -1.438972, rho = 0.020031
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.184725
obj = -1.650280, rho = 0.064033
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*...*
optimization finished, #iter = 391
nu = 0.165728
obj = -1.889723, rho = 0.163972
nSV = 23, nBSV = 13
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.148433
obj = -2.181225, rho = 0.128260
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.136834
obj = -2.506357, rho = 0.120015
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.125043
obj = -2.859309, rho = 0.179744
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.111958
obj = -3.250313, rho = 0.226463
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.100718
obj = -3.682690, rho = 0.324548
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
....*.....*
optimization finished, #iter = 949
nu = 0.089978
obj = -4.152665, rho = 0.492247
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.......*
optimization finished, #iter = 897
nu = 0.082753
obj = -4.651912, rho = 0.596264
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*
optimization finished, #iter = 297
nu = 0.075985
obj = -5.069627, rho = 0.736686
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*
optimization finished, #iter = 224
nu = 0.064089
obj = -5.457714, rho = 0.803263
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.057170
obj = -5.838081, rho = 0.807619
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.049966
obj = -5.994527, rho = 0.843321
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.040373
obj = -6.009775, rho = 0.907635
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.031683
obj = -6.009775, rho = 0.907635
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.024864
obj = -6.009775, rho = 0.907635
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.019512
obj = -6.009775, rho = 0.907635
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.015312
obj = -6.009775, rho = 0.907635
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.012016
obj = -6.009775, rho = 0.907635
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 194
nu = 0.199819
obj = -1.249303, rho = -0.163500
nSV = 26, nBSV = 16
Total nSV = 26
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.171026
obj = -1.380511, rho = -0.163737
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.148614
obj = -1.528857, rho = -0.169608
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 458
nu = 0.129746
obj = -1.685453, rho = -0.146636
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*................*..*
optimization finished, #iter = 1855
nu = 0.110257
obj = -1.869477, rho = -0.132790
nSV = 18, nBSV = 7
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
...*
optimization finished, #iter = 359
nu = 0.095628
obj = -2.087914, rho = -0.099861
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 205
nu = 0.083693
obj = -2.338817, rho = -0.088958
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.074763
obj = -2.606823, rho = -0.090444
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 63
nu = 0.066980
obj = -2.888778, rho = -0.237493
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 176
nu = 0.059247
obj = -3.139895, rho = -0.440168
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.050208
obj = -3.412757, rho = -0.490149
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.043252
obj = -3.687843, rho = -0.601658
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.039711
obj = -3.902587, rho = -0.536100
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.033722
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.026464
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.020768
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.016298
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.012790
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.010037
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.007877
obj = -3.938339, rho = -0.585109
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.221734
obj = -1.410660, rho = -0.572475
nSV = 27, nBSV = 20
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.**.*
optimization finished, #iter = 171
nu = 0.194873
obj = -1.556080, rho = -0.614730
nSV = 25, nBSV = 15
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.167654
obj = -1.721264, rho = -0.631443
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*..*
optimization finished, #iter = 311
nu = 0.144940
obj = -1.906537, rho = -0.632168
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.127175
obj = -2.101794, rho = -0.647016
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 181
nu = 0.112493
obj = -2.314051, rho = -0.740314
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*..*
optimization finished, #iter = 385
nu = 0.097776
obj = -2.512461, rho = -0.856371
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
...*
optimization finished, #iter = 395
nu = 0.082318
obj = -2.731917, rho = -0.945148
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*.*
optimization finished, #iter = 461
nu = 0.069005
obj = -2.992815, rho = -0.965666
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
..*.*
optimization finished, #iter = 363
nu = 0.057904
obj = -3.316762, rho = -0.956536
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.
WARNING: using -h 0 may be faster
*
optimization finished, #iter = 153
nu = 0.050887
obj = -3.708611, rho = -0.921658
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*..*
optimization finished, #iter = 419
nu = 0.045403
obj = -4.113790, rho = -0.853200
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*..*
optimization finished, #iter = 494
nu = 0.038189
obj = -4.578415, rho = -0.855667
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*..*
optimization finished, #iter = 412
nu = 0.033371
obj = -5.163685, rho = -0.879470
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*
optimization finished, #iter = 278
nu = 0.030770
obj = -5.776758, rho = -0.963863
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*.*
optimization finished, #iter = 328
nu = 0.029646
obj = -6.237641, rho = -1.084670
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
....*.....*
optimization finished, #iter = 957
nu = 0.026229
obj = -6.337372, rho = -1.118443
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
....*.....*
optimization finished, #iter = 957
nu = 0.020583
obj = -6.337372, rho = -1.118443
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
....*.....*
optimization finished, #iter = 957
nu = 0.016153
obj = -6.337372, rho = -1.118443
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
....*.....*
optimization finished, #iter = 957
nu = 0.012676
obj = -6.337372, rho = -1.118443
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.190467
obj = -1.406639, rho = -0.125803
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.179343
obj = -1.641801, rho = -0.060048
nSV = 20, nBSV = 16
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.163394
obj = -1.899306, rho = -0.022257
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 125
nu = 0.145717
obj = -2.205266, rho = -0.048997
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.133559
obj = -2.571040, rho = -0.147962
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.120925
obj = -2.993363, rho = -0.188613
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.108390
obj = -3.513528, rho = -0.151233
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 40
nu = 0.100305
obj = -4.147163, rho = -0.107829
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.096671
obj = -4.856444, rho = 0.130645
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*....*
optimization finished, #iter = 637
nu = 0.089431
obj = -5.605207, rho = 0.324182
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*...*
optimization finished, #iter = 515
nu = 0.079060
obj = -6.493604, rho = 0.353786
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*..*
optimization finished, #iter = 491
nu = 0.075239
obj = -7.484520, rho = 0.246394
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 194
nu = 0.070207
obj = -8.458156, rho = -0.017286
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.060404
obj = -9.549853, rho = 0.002763
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 150
nu = 0.052708
obj = -10.889378, rho = 0.028444
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.046610
obj = -12.513157, rho = 0.059657
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 284
nu = 0.041640
obj = -14.469592, rho = 0.096087
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 318
nu = 0.039307
obj = -16.694033, rho = 0.252230
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*
optimization finished, #iter = 251
nu = 0.038031
obj = -18.793460, rho = 0.485273
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 243
nu = 0.037296
obj = -20.185982, rho = 0.767261
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.210149
obj = -1.350768, rho = -0.309738
nSV = 26, nBSV = 17
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.187508
obj = -1.499048, rho = -0.393400
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.160722
obj = -1.657021, rho = -0.482004
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 153
nu = 0.139893
obj = -1.839493, rho = -0.476113
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.124182
obj = -2.036051, rho = -0.485949
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 162
nu = 0.108667
obj = -2.224399, rho = -0.452568
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.091772
obj = -2.436175, rho = -0.431730
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 246
nu = 0.077633
obj = -2.689812, rho = -0.462605
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.069056
obj = -2.982910, rho = -0.512775
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*...*
optimization finished, #iter = 446
nu = 0.062236
obj = -3.226771, rho = -0.573489
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.......*
optimization finished, #iter = 925
nu = 0.051129
obj = -3.484825, rho = -0.573769
nSV = 13, nBSV = 1
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.....*.....*
optimization finished, #iter = 1053
nu = 0.042407
obj = -3.813347, rho = -0.573625
nSV = 13, nBSV = 1
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..........*..*
optimization finished, #iter = 1284
nu = 0.035566
obj = -4.231926, rho = -0.573874
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 220
nu = 0.033353
obj = -4.670952, rho = -0.641218
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.031924
obj = -4.920226, rho = -0.729683
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.025991
obj = -4.927762, rho = -0.748218
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.020397
obj = -4.927762, rho = -0.748218
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.016006
obj = -4.927762, rho = -0.748218
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.012561
obj = -4.927762, rho = -0.748218
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.009857
obj = -4.927762, rho = -0.748218
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.220962
obj = -1.479445, rho = -0.073303
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 154
nu = 0.191508
obj = -1.682394, rho = -0.094544
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.170763
obj = -1.928123, rho = -0.104912
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*...*
optimization finished, #iter = 481
nu = 0.154788
obj = -2.199628, rho = -0.139417
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*...*
optimization finished, #iter = 412
nu = 0.135044
obj = -2.523711, rho = -0.146587
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.122073
obj = -2.915834, rho = -0.095653
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.108849
obj = -3.379554, rho = -0.162487
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.099402
obj = -3.923981, rho = -0.211703
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.089878
obj = -4.565930, rho = -0.194853
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.081341
obj = -5.323854, rho = -0.175129
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.073310
obj = -6.253556, rho = -0.196240
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.068911
obj = -7.365003, rho = -0.204356
nSV = 11, nBSV = 5
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.065059
obj = -8.565792, rho = -0.293748
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.060023
obj = -9.902376, rho = -0.298885
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.057190
obj = -11.324065, rho = -0.344852
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.055885
obj = -12.492547, rho = -0.502629
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.050323
obj = -13.145082, rho = -0.482033
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.043400
obj = -13.367439, rho = -0.362702
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.034059
obj = -13.367439, rho = -0.362702
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
.*.*
optimization finished, #iter = 202
nu = 0.026728
obj = -13.367439, rho = -0.362702
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.4% (964/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.176350
obj = -1.061884, rho = -0.364706
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.150997
obj = -1.147348, rho = -0.402639
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.127330
obj = -1.240944, rho = -0.401918
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.109325
obj = -1.345386, rho = -0.433449
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.093901
obj = -1.450040, rho = -0.398047
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.081654
obj = -1.545390, rho = -0.320662
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.067760
obj = -1.632251, rho = -0.331904
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.057850
obj = -1.715718, rho = -0.392408
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.049554
obj = -1.772568, rho = -0.525247
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.040094
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.031464
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.024692
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.019377
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.015207
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.011933
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.009365
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.007349
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.005767
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.004526
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 137
nu = 0.003552
obj = -1.775843, rho = -0.579470
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.194463
obj = -1.328176, rho = -0.138099
nSV = 25, nBSV = 16
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 161
nu = 0.173291
obj = -1.513880, rho = -0.160933
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.153419
obj = -1.730653, rho = -0.155642
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.137542
obj = -1.983430, rho = -0.234110
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.120501
obj = -2.286465, rho = -0.289233
nSV = 18, nBSV = 8
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.107533
obj = -2.662338, rho = -0.380173
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 37
nu = 0.102087
obj = -3.098036, rho = -0.519192
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.098561
obj = -3.527486, rho = -0.680713
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
..*.*
optimization finished, #iter = 326
nu = 0.086865
obj = -3.938875, rho = -0.762275
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*..*
optimization finished, #iter = 420
nu = 0.075557
obj = -4.429320, rho = -0.877354
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 162
nu = 0.068889
obj = -4.947318, rho = -1.153021
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.060179
obj = -5.473157, rho = -1.366953
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 148
nu = 0.052787
obj = -6.076103, rho = -1.471454
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 383
nu = 0.049684
obj = -6.584908, rho = -1.811501
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*.*
optimization finished, #iter = 237
nu = 0.045260
obj = -6.804272, rho = -2.039588
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.035929
obj = -6.813063, rho = -2.141435
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*..................*
optimization finished, #iter = 1933
nu = 0.028195
obj = -6.813035, rho = -2.143515
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*..................*
optimization finished, #iter = 1933
nu = 0.022126
obj = -6.813035, rho = -2.143515
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*..................*
optimization finished, #iter = 1933
nu = 0.017364
obj = -6.813035, rho = -2.143515
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
.*..................*
optimization finished, #iter = 1933
nu = 0.013626
obj = -6.813035, rho = -2.143515
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.6% (956/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.228696
obj = -1.513454, rho = -0.284413
nSV = 28, nBSV = 20
Total nSV = 28
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.203849
obj = -1.699973, rho = -0.252628
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.176665
obj = -1.910746, rho = -0.268489
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.157320
obj = -2.158776, rho = -0.206670
nSV = 19, nBSV = 13
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.137686
obj = -2.433632, rho = -0.212598
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.121326
obj = -2.764637, rho = -0.183097
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.107745
obj = -3.140217, rho = -0.137597
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 179
nu = 0.098384
obj = -3.557307, rho = -0.103400
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 229
nu = 0.088639
obj = -3.977051, rho = -0.074794
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.......*
optimization finished, #iter = 836
nu = 0.077643
obj = -4.438432, rho = -0.112930
nSV = 15, nBSV = 4
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.069836
obj = -4.946846, rho = -0.242778
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.064512
obj = -5.368056, rho = -0.450812
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.054357
obj = -5.757240, rho = -0.466721
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
..*.*
optimization finished, #iter = 338
nu = 0.048959
obj = -6.017037, rho = -0.554465
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.040875
obj = -6.083092, rho = -0.586678
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.032077
obj = -6.083092, rho = -0.586678
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.025173
obj = -6.083092, rho = -0.586678
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.019755
obj = -6.083092, rho = -0.586678
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.015503
obj = -6.083092, rho = -0.586678
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.012166
obj = -6.083092, rho = -0.586678
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.212497
obj = -1.552656, rho = -0.150011
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.191801
obj = -1.812002, rho = -0.131817
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 97% (97/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.176472
obj = -2.126083, rho = -0.087354
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.164526
obj = -2.482377, rho = 0.041198
nSV = 22, nBSV = 12
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 128
nu = 0.151867
obj = -2.880105, rho = 0.136795
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.137779
obj = -3.345939, rho = 0.144610
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.127259
obj = -3.867816, rho = 0.147038
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 350
nu = 0.112641
obj = -4.474154, rho = 0.153262
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
......*..*
optimization finished, #iter = 836
nu = 0.099634
obj = -5.240566, rho = 0.166220
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.**..............*
optimization finished, #iter = 1573
nu = 0.089290
obj = -6.214214, rho = 0.205513
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.**.*
optimization finished, #iter = 189
nu = 0.082546
obj = -7.429229, rho = 0.083713
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*.*
optimization finished, #iter = 206
nu = 0.078551
obj = -8.886819, rho = 0.013964
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.......*
optimization finished, #iter = 859
nu = 0.074511
obj = -10.568259, rho = -0.172891
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 392
nu = 0.069782
obj = -12.543097, rho = -0.500090
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
..*.*
optimization finished, #iter = 302
nu = 0.066183
obj = -14.812930, rho = -0.970344
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*.*
optimization finished, #iter = 239
nu = 0.063830
obj = -17.290521, rho = -1.549896
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
..*.*
optimization finished, #iter = 321
nu = 0.060081
obj = -19.765428, rho = -2.093275
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*.*
optimization finished, #iter = 484
nu = 0.051825
obj = -22.645856, rho = -2.093138
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
...*.*
optimization finished, #iter = 443
nu = 0.045970
obj = -26.286479, rho = -2.328368
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.1% (951/1000) (classification)
.....*.*
optimization finished, #iter = 665
nu = 0.041988
obj = -30.622874, rho = -2.924132
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 123
nu = 0.169144
obj = -1.055265, rho = -0.223178
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.144891
obj = -1.161941, rho = -0.248817
nSV = 21, nBSV = 10
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 215
nu = 0.122646
obj = -1.290117, rho = -0.271374
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 142
nu = 0.106964
obj = -1.440613, rho = -0.320327
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.092181
obj = -1.622304, rho = -0.280446
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.084429
obj = -1.821823, rho = -0.109934
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.077506
obj = -2.009144, rho = -0.009487
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.067132
obj = -2.170214, rho = 0.006873
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.056930
obj = -2.341012, rho = 0.096216
nSV = 10, nBSV = 4
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.048910
obj = -2.514223, rho = 0.198650
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 126
nu = 0.045096
obj = -2.634369, rho = -0.071075
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.036693
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.028796
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.022598
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.017734
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.013917
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.010921
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.008571
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.006726
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.005278
obj = -2.638938, rho = -0.136416
nSV = 7, nBSV = 0
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.239972
obj = -1.653716, rho = -0.007648
nSV = 27, nBSV = 21
Total nSV = 27
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.215522
obj = -1.886159, rho = 0.117522
nSV = 23, nBSV = 18
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*..*
optimization finished, #iter = 221
nu = 0.195519
obj = -2.138131, rho = 0.081982
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.169699
obj = -2.432978, rho = 0.096753
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.150151
obj = -2.797558, rho = 0.025035
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.138991
obj = -3.211853, rho = -0.073585
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*..*
optimization finished, #iter = 305
nu = 0.123661
obj = -3.674624, rho = -0.021361
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.111893
obj = -4.190202, rho = -0.000482
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.102591
obj = -4.755052, rho = -0.067845
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.093051
obj = -5.344066, rho = -0.070551
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.084862
obj = -5.908290, rho = -0.022673
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.076077
obj = -6.409785, rho = -0.131096
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*.*
optimization finished, #iter = 144
nu = 0.062861
obj = -6.915853, rho = -0.149866
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 116
nu = 0.055443
obj = -7.472307, rho = -0.278221
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.3% (953/1000) (classification)
.*
optimization finished, #iter = 160
nu = 0.050666
obj = -7.728819, rho = -0.413062
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 348
nu = 0.040860
obj = -7.748948, rho = -0.470536
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 348
nu = 0.032065
obj = -7.748948, rho = -0.470536
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 348
nu = 0.025163
obj = -7.748948, rho = -0.470536
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 348
nu = 0.019747
obj = -7.748948, rho = -0.470536
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
..*.*
optimization finished, #iter = 348
nu = 0.015497
obj = -7.748948, rho = -0.470536
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.4% (944/1000) (classification)
*
optimization finished, #iter = 87
nu = 0.190350
obj = -1.240441, rho = -0.223785
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 189
nu = 0.170517
obj = -1.385977, rho = -0.269531
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 186
nu = 0.151374
obj = -1.527572, rho = -0.321361
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 257
nu = 0.129160
obj = -1.683733, rho = -0.297722
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.110636
obj = -1.861720, rho = -0.330247
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.095527
obj = -2.074678, rho = -0.326134
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.082962
obj = -2.324728, rho = -0.380908
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.072586
obj = -2.614552, rho = -0.534305
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.064682
obj = -2.942684, rho = -0.419449
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.061236
obj = -3.251464, rho = -0.041440
nSV = 9, nBSV = 3
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.054566
obj = -3.460026, rho = 0.142116
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*...*
optimization finished, #iter = 603
nu = 0.046539
obj = -3.612218, rho = 0.167010
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 388
nu = 0.040125
obj = -3.677300, rho = 0.215351
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.031488
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.024710
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.019392
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.015218
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.011942
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.009372
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
...*.*
optimization finished, #iter = 389
nu = 0.007355
obj = -3.677300, rho = 0.215533
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.177485
obj = -1.163966, rho = -0.156034
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.159211
obj = -1.301058, rho = -0.175122
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
.*
optimization finished, #iter = 188
nu = 0.141066
obj = -1.442991, rho = -0.145564
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.122418
obj = -1.599672, rho = -0.044587
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.111570
obj = -1.753517, rho = 0.168321
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*..*
optimization finished, #iter = 387
nu = 0.095684
obj = -1.882090, rho = 0.245962
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 293
nu = 0.082618
obj = -2.001563, rho = 0.209931
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*
optimization finished, #iter = 248
nu = 0.068378
obj = -2.120526, rho = 0.261803
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 233
nu = 0.056107
obj = -2.258277, rho = 0.302402
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*..*
optimization finished, #iter = 335
nu = 0.046662
obj = -2.415215, rho = 0.367450
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 240
nu = 0.039467
obj = -2.594201, rho = 0.450244
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 223
nu = 0.035247
obj = -2.738230, rho = 0.451748
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.030241
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.023732
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.018624
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.014615
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.011470
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.009001
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.007063
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.005543
obj = -2.771645, rho = 0.455898
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.189158
obj = -1.216219, rho = -0.352499
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.161773
obj = -1.360602, rho = -0.375524
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.143852
obj = -1.522325, rho = -0.392649
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 81
nu = 0.124562
obj = -1.710958, rho = -0.377980
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.110739
obj = -1.935072, rho = -0.319576
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.102851
obj = -2.157442, rho = -0.154247
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.089077
obj = -2.375247, rho = -0.217560
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*..*
optimization finished, #iter = 204
nu = 0.076897
obj = -2.609878, rho = -0.322269
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*..*
optimization finished, #iter = 221
nu = 0.065383
obj = -2.881115, rho = -0.371677
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
...*..*
optimization finished, #iter = 592
nu = 0.055705
obj = -3.206325, rho = -0.401430
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*...*
optimization finished, #iter = 572
nu = 0.047526
obj = -3.608375, rho = -0.410326
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 269
nu = 0.041520
obj = -4.110089, rho = -0.440174
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.037603
obj = -4.681275, rho = -0.516528
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 211
nu = 0.034799
obj = -5.271374, rho = -0.507258
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.031603
obj = -5.857377, rho = -0.586784
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.029267
obj = -6.345257, rho = -0.705949
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.027205
obj = -6.571668, rho = -0.736422
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.021349
obj = -6.571668, rho = -0.736422
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.016754
obj = -6.571668, rho = -0.736422
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.013148
obj = -6.571668, rho = -0.736422
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.211335
obj = -1.443078, rho = -0.135435
nSV = 25, nBSV = 19
Total nSV = 25
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.192568
obj = -1.634303, rho = -0.159330
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.176531
obj = -1.829824, rho = 0.002088
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 156
nu = 0.154122
obj = -2.039825, rho = 0.002701
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 189
nu = 0.132551
obj = -2.273593, rho = 0.007967
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.118081
obj = -2.536723, rho = -0.034106
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 111
nu = 0.104166
obj = -2.814237, rho = -0.078365
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*..*
optimization finished, #iter = 209
nu = 0.089185
obj = -3.112241, rho = -0.111005
nSV = 16, nBSV = 5
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 136
nu = 0.078253
obj = -3.448774, rho = -0.143887
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.069143
obj = -3.818269, rho = -0.210872
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.060403
obj = -4.182436, rho = -0.211121
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 146
nu = 0.052110
obj = -4.583320, rho = -0.289503
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.047281
obj = -4.944071, rho = -0.399333
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*..*
optimization finished, #iter = 435
nu = 0.040958
obj = -5.184010, rho = -0.398790
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.035554
obj = -5.291656, rho = -0.391245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.027901
obj = -5.291656, rho = -0.391245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.021896
obj = -5.291656, rho = -0.391245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.017183
obj = -5.291656, rho = -0.391245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.013485
obj = -5.291656, rho = -0.391245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 208
nu = 0.010582
obj = -5.291656, rho = -0.391245
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.248821
obj = -1.827531, rho = -0.021760
nSV = 29, nBSV = 19
Total nSV = 29
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.222026
obj = -2.145107, rho = -0.029566
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 96% (96/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 58
nu = 0.202560
obj = -2.535857, rho = -0.110126
nSV = 25, nBSV = 17
Total nSV = 25
Accuracy = 95% (95/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.189941
obj = -2.997499, rho = -0.263663
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 96% (96/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.174313
obj = -3.551002, rho = -0.298788
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 97% (97/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.164977
obj = -4.200197, rho = -0.381214
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.151709
obj = -4.970826, rho = -0.445026
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.141904
obj = -5.850759, rho = -0.483683
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 242
nu = 0.126998
obj = -6.936463, rho = -0.475759
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.116874
obj = -8.299012, rho = -0.497806
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.108985
obj = -9.986820, rho = -0.583319
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 161
nu = 0.103912
obj = -12.030894, rho = -0.668075
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 97% (97/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.099989
obj = -14.408216, rho = -0.630555
nSV = 14, nBSV = 6
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.094958
obj = -17.135179, rho = -0.632853
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 219
nu = 0.088628
obj = -20.311833, rho = -0.693364
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*..*
optimization finished, #iter = 530
nu = 0.081995
obj = -24.069060, rho = -0.781589
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*..*
optimization finished, #iter = 326
nu = 0.074015
obj = -28.741271, rho = -0.781832
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.071956
obj = -34.360420, rho = -1.068891
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
...*.*
optimization finished, #iter = 419
nu = 0.070046
obj = -40.462192, rho = -1.422080
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
....*.*
optimization finished, #iter = 570
nu = 0.064778
obj = -47.215172, rho = -1.596909
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.175000
obj = -1.240545, rho = -0.054296
nSV = 21, nBSV = 14
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.157499
obj = -1.432732, rho = 0.040099
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 142
nu = 0.142505
obj = -1.659794, rho = 0.054714
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.133180
obj = -1.912882, rho = 0.049131
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 151
nu = 0.124288
obj = -2.171562, rho = 0.057221
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 228
nu = 0.111595
obj = -2.433033, rho = -0.036779
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
..*..*
optimization finished, #iter = 452
nu = 0.097840
obj = -2.726191, rho = -0.026111
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.085386
obj = -3.049206, rho = -0.052914
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.074009
obj = -3.433091, rho = -0.067926
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 129
nu = 0.064808
obj = -3.879983, rho = -0.003759
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 218
nu = 0.057977
obj = -4.402482, rho = 0.089896
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.055341
obj = -4.929246, rho = 0.378007
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 154
nu = 0.050962
obj = -5.278204, rho = 0.633645
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.042307
obj = -5.582949, rho = 0.371900
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 115
nu = 0.035713
obj = -5.885475, rho = 0.016204
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.031600
obj = -5.993678, rho = 0.020949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.024798
obj = -5.993678, rho = 0.020949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.019461
obj = -5.993678, rho = 0.020949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.015272
obj = -5.993678, rho = 0.020949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.011985
obj = -5.993678, rho = 0.020949
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.191565
obj = -1.259786, rho = -0.190046
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*.*
optimization finished, #iter = 143
nu = 0.168619
obj = -1.415009, rho = -0.248694
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 231
nu = 0.149256
obj = -1.583795, rho = -0.255437
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.129277
obj = -1.783469, rho = -0.218977
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.117236
obj = -2.008203, rho = -0.206591
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.105828
obj = -2.220723, rho = -0.331405
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 215
nu = 0.092560
obj = -2.440299, rho = -0.318776
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.079433
obj = -2.665158, rho = -0.287929
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.067690
obj = -2.927504, rho = -0.336899
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.059073
obj = -3.215572, rho = -0.447110
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.053496
obj = -3.474091, rho = -0.530293
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.047616
obj = -3.646364, rho = -0.616248
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.038575
obj = -3.749307, rho = -0.582836
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.031330
obj = -3.848472, rho = -0.534472
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.025028
obj = -3.944371, rho = -0.519602
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.021248
obj = -4.030119, rho = -0.407277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.016675
obj = -4.030119, rho = -0.407277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.013086
obj = -4.030119, rho = -0.407277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.010269
obj = -4.030119, rho = -0.407277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*.*
optimization finished, #iter = 227
nu = 0.008059
obj = -4.030119, rho = -0.407277
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 75
nu = 0.179543
obj = -1.117272, rho = -0.054259
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*.*
optimization finished, #iter = 152
nu = 0.155811
obj = -1.225904, rho = -0.063737
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.136577
obj = -1.334822, rho = -0.083291
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 183
nu = 0.113840
obj = -1.457613, rho = -0.089184
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 80
nu = 0.099642
obj = -1.597827, rho = -0.082220
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.091885
obj = -1.713013, rho = -0.118587
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.079151
obj = -1.757761, rho = -0.200652
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.063576
obj = -1.786414, rho = -0.201970
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*....*
optimization finished, #iter = 508
nu = 0.050847
obj = -1.806662, rho = -0.191568
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*....*
optimization finished, #iter = 733
nu = 0.040654
obj = -1.824331, rho = -0.204868
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.032389
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.025418
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.019947
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.015653
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.012284
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.009640
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.007565
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.005937
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.004659
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.....*.......*...*
optimization finished, #iter = 1441
nu = 0.003656
obj = -1.827892, rho = -0.215173
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 34
nu = 0.214059
obj = -1.506390, rho = -0.176580
nSV = 24, nBSV = 19
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.194402
obj = -1.729050, rho = -0.263477
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 46
nu = 0.177328
obj = -1.981323, rho = -0.249979
nSV = 21, nBSV = 15
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.159647
obj = -2.251251, rho = -0.231265
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.138063
obj = -2.579036, rho = -0.218883
nSV = 21, nBSV = 11
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.129729
obj = -2.961611, rho = -0.077134
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.117347
obj = -3.355804, rho = -0.119449
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*..*
optimization finished, #iter = 261
nu = 0.107195
obj = -3.771667, rho = -0.174711
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 148
nu = 0.095473
obj = -4.186635, rho = -0.056312
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.084442
obj = -4.622208, rho = 0.013162
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 222
nu = 0.071515
obj = -5.077406, rho = 0.026088
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.066390
obj = -5.533260, rho = 0.178070
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.061026
obj = -5.778507, rho = 0.308019
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.049555
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.038889
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.030519
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.023950
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.018795
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.014749
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*
optimization finished, #iter = 140
nu = 0.011575
obj = -5.788208, rho = 0.336793
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.198528
obj = -1.313403, rho = -0.304106
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.176382
obj = -1.472500, rho = -0.149796
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.152058
obj = -1.659347, rho = -0.174725
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.138130
obj = -1.870341, rho = -0.292586
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 38
nu = 0.120834
obj = -2.102585, rho = -0.297639
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.108951
obj = -2.354482, rho = -0.297236
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
..*.*
optimization finished, #iter = 255
nu = 0.095594
obj = -2.621880, rho = -0.301897
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.083144
obj = -2.912215, rho = -0.322518
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 108
nu = 0.072534
obj = -3.247972, rho = -0.399969
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.064037
obj = -3.612778, rho = -0.494654
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 156
nu = 0.058904
obj = -3.951797, rho = -0.433062
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*
optimization finished, #iter = 222
nu = 0.050352
obj = -4.253988, rho = -0.602680
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 248
nu = 0.043224
obj = -4.559176, rho = -0.842115
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*
optimization finished, #iter = 159
nu = 0.038473
obj = -4.799308, rho = -1.368819
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.032439
obj = -4.827054, rho = -1.620569
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.025457
obj = -4.827054, rho = -1.620569
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.019978
obj = -4.827054, rho = -1.620569
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.015678
obj = -4.827054, rho = -1.620569
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.012303
obj = -4.827054, rho = -1.620569
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.009655
obj = -4.827054, rho = -1.620569
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.195947
obj = -1.245511, rho = 0.062535
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 84
nu = 0.166659
obj = -1.387979, rho = 0.061949
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.148505
obj = -1.556896, rho = 0.035881
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.128658
obj = -1.743620, rho = 0.128348
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*....*
optimization finished, #iter = 509
nu = 0.112899
obj = -1.960694, rho = 0.182427
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.098383
obj = -2.213820, rho = 0.231961
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 76
nu = 0.086656
obj = -2.505602, rho = 0.275839
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 167
nu = 0.077075
obj = -2.844332, rho = 0.267150
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.067662
obj = -3.238156, rho = 0.281869
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.061318
obj = -3.688103, rho = 0.268335
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.057109
obj = -4.155434, rho = 0.242543
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.054312
obj = -4.554830, rho = 0.173740
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*...*
optimization finished, #iter = 582
nu = 0.049091
obj = -4.749047, rho = 0.090932
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.041250
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.032371
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.025404
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.019936
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.015645
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.012277
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
...*.*
optimization finished, #iter = 431
nu = 0.009635
obj = -4.818230, rho = 0.094355
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 54
nu = 0.181333
obj = -1.164018, rho = -0.122189
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.158511
obj = -1.294119, rho = -0.111347
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 131
nu = 0.144408
obj = -1.428254, rho = -0.037840
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.123805
obj = -1.553569, rho = 0.013141
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.106351
obj = -1.692532, rho = 0.032407
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.092557
obj = -1.828528, rho = 0.030526
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.077239
obj = -1.972840, rho = 0.067012
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 99
nu = 0.065552
obj = -2.134978, rho = 0.132302
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.057955
obj = -2.289950, rho = 0.177778
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.048133
obj = -2.429600, rho = 0.120315
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 91
nu = 0.040864
obj = -2.571834, rho = 0.102198
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 134
nu = 0.035347
obj = -2.665784, rho = 0.119132
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.029351
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.023033
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.018076
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.014185
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.011132
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.008736
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.006856
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.005380
obj = -2.690535, rho = 0.066787
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.191744
obj = -1.194464, rho = -0.234021
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 82
nu = 0.165953
obj = -1.311152, rho = -0.233911
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.140145
obj = -1.445962, rho = -0.209578
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.120225
obj = -1.609166, rho = -0.187021
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 123
nu = 0.105736
obj = -1.795107, rho = -0.285182
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.091825
obj = -1.999617, rho = -0.254590
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.080849
obj = -2.234641, rho = -0.222205
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*
optimization finished, #iter = 177
nu = 0.072222
obj = -2.483027, rho = -0.260974
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 166
nu = 0.063992
obj = -2.710231, rho = -0.316151
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.056762
obj = -2.931437, rho = -0.356505
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.046817
obj = -3.153937, rho = -0.337148
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.040067
obj = -3.418429, rho = -0.180873
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 183
nu = 0.035020
obj = -3.663823, rho = -0.009610
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.031767
obj = -3.809889, rho = 0.205711
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.025631
obj = -3.814418, rho = 0.250483
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.020114
obj = -3.814418, rho = 0.250483
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.015785
obj = -3.814418, rho = 0.250483
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.012387
obj = -3.814418, rho = 0.250483
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.009721
obj = -3.814418, rho = 0.250483
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 175
nu = 0.007629
obj = -3.814418, rho = 0.250483
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*.*
optimization finished, #iter = 270
nu = 0.180870
obj = -1.220894, rho = -0.256584
nSV = 23, nBSV = 14
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.161020
obj = -1.387596, rho = -0.333853
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.145443
obj = -1.567371, rho = -0.345406
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.128515
obj = -1.762094, rho = -0.311872
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.114670
obj = -1.983160, rho = -0.274061
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 83
nu = 0.105675
obj = -2.210073, rho = -0.234022
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 175
nu = 0.093091
obj = -2.414173, rho = -0.183048
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.077527
obj = -2.640607, rho = -0.183172
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.065443
obj = -2.925267, rho = -0.181399
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 124
nu = 0.057943
obj = -3.263291, rho = -0.309267
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.050439
obj = -3.607615, rho = -0.403114
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.044359
obj = -3.987606, rho = -0.329686
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 185
nu = 0.039673
obj = -4.364302, rho = -0.237339
nSV = 9, nBSV = 2
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
..*..*
optimization finished, #iter = 435
nu = 0.036283
obj = -4.641755, rho = -0.168391
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 209
nu = 0.030292
obj = -4.810263, rho = -0.205169
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.025578
obj = -4.850292, rho = -0.237798
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.020072
obj = -4.850292, rho = -0.237798
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.015752
obj = -4.850292, rho = -0.237798
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.012361
obj = -4.850292, rho = -0.237798
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*.*
optimization finished, #iter = 266
nu = 0.009701
obj = -4.850292, rho = -0.237798
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 47
nu = 0.194518
obj = -1.331238, rho = -0.074181
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.174101
obj = -1.517607, rho = -0.099424
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.155077
obj = -1.728572, rho = -0.170722
nSV = 19, nBSV = 12
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 197
nu = 0.135669
obj = -1.981213, rho = -0.187747
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..*
optimization finished, #iter = 394
nu = 0.120227
obj = -2.290999, rho = -0.213535
nSV = 17, nBSV = 8
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 196
nu = 0.109740
obj = -2.664941, rho = -0.233168
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.**.*
optimization finished, #iter = 137
nu = 0.101086
obj = -3.081837, rho = -0.250080
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.092345
obj = -3.547227, rho = -0.207336
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*...*
optimization finished, #iter = 378
nu = 0.085999
obj = -4.049214, rho = -0.154261
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
...*.*
optimization finished, #iter = 470
nu = 0.076126
obj = -4.587803, rho = -0.122246
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.......*.*
optimization finished, #iter = 873
nu = 0.066860
obj = -5.249967, rho = -0.085018
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
...*..............*
optimization finished, #iter = 1754
nu = 0.060188
obj = -6.016124, rho = -0.018116
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
....*.*
optimization finished, #iter = 530
nu = 0.055909
obj = -6.857182, rho = 0.162220
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
...*.*
optimization finished, #iter = 429
nu = 0.050163
obj = -7.697934, rho = 0.244271
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
...*.....*
optimization finished, #iter = 858
nu = 0.043646
obj = -8.661216, rho = 0.191441
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
....*.*
optimization finished, #iter = 514
nu = 0.037626
obj = -9.845993, rho = 0.180427
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
........*....*
optimization finished, #iter = 1270
nu = 0.033104
obj = -11.278507, rho = 0.153715
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
...*.*
optimization finished, #iter = 467
nu = 0.029031
obj = -13.074094, rho = 0.171243
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
..*..*
optimization finished, #iter = 409
nu = 0.027163
obj = -15.182901, rho = 0.172181
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 95% (950/1000) (classification)
..*.*
optimization finished, #iter = 394
nu = 0.025683
obj = -17.407448, rho = 0.193528
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 94.6% (946/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.168771
obj = -1.042565, rho = -0.149735
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.144982
obj = -1.147394, rho = -0.173602
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 99% (990/1000) (classification)
.*
optimization finished, #iter = 158
nu = 0.125094
obj = -1.260701, rho = -0.213181
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 90
nu = 0.105587
obj = -1.391644, rho = -0.187042
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 72
nu = 0.093230
obj = -1.544136, rho = -0.232224
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.088807
obj = -1.670021, rho = -0.427942
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
.*.*
optimization finished, #iter = 283
nu = 0.073874
obj = -1.749056, rho = -0.460202
nSV = 14, nBSV = 3
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.060170
obj = -1.834864, rho = -0.458846
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*..*
optimization finished, #iter = 558
nu = 0.049874
obj = -1.927265, rho = -0.475880
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
...*.*
optimization finished, #iter = 416
nu = 0.041343
obj = -2.019492, rho = -0.481230
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
..*
optimization finished, #iter = 296
nu = 0.034153
obj = -2.113257, rho = -0.507839
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*...*
optimization finished, #iter = 591
nu = 0.029030
obj = -2.175981, rho = -0.578071
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.023894
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.018751
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.014715
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.011548
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.009062
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.007112
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.005581
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*.*
optimization finished, #iter = 301
nu = 0.004380
obj = -2.189456, rho = -0.614876
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 106
nu = 0.205697
obj = -1.419968, rho = -0.468219
nSV = 24, nBSV = 15
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.184951
obj = -1.622248, rho = -0.509850
nSV = 24, nBSV = 14
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 49
nu = 0.164313
obj = -1.856831, rho = -0.557124
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 98% (98/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.150879
obj = -2.121222, rho = -0.606956
nSV = 19, nBSV = 11
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*..........*
optimization finished, #iter = 1107
nu = 0.133572
obj = -2.407422, rho = -0.645710
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*...*
optimization finished, #iter = 396
nu = 0.116080
obj = -2.757714, rho = -0.642010
nSV = 19, nBSV = 8
Total nSV = 19
Accuracy = 97% (97/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 204
nu = 0.102380
obj = -3.192553, rho = -0.635758
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 97% (97/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 171
nu = 0.092995
obj = -3.724713, rho = -0.591677
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 117
nu = 0.086765
obj = -4.334329, rho = -0.473123
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.079444
obj = -5.011349, rho = -0.387512
nSV = 12, nBSV = 5
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 78
nu = 0.074135
obj = -5.761406, rho = -0.212042
nSV = 11, nBSV = 4
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.068087
obj = -6.548497, rho = -0.167095
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
.*
optimization finished, #iter = 145
nu = 0.062261
obj = -7.334567, rho = -0.124928
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.056089
obj = -8.113139, rho = -0.264325
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 104
nu = 0.050897
obj = -8.807343, rho = -0.465450
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 225
nu = 0.046546
obj = -9.222124, rho = -0.696288
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*
optimization finished, #iter = 285
nu = 0.038291
obj = -9.254186, rho = -0.761944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*
optimization finished, #iter = 285
nu = 0.030049
obj = -9.254186, rho = -0.761944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*
optimization finished, #iter = 285
nu = 0.023581
obj = -9.254186, rho = -0.761944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*
optimization finished, #iter = 285
nu = 0.018506
obj = -9.254186, rho = -0.761944
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.163193
obj = -1.022124, rho = -0.276778
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 42
nu = 0.142121
obj = -1.124763, rho = -0.260221
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 43
nu = 0.126483
obj = -1.223029, rho = -0.138206
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 56
nu = 0.110472
obj = -1.314253, rho = -0.046145
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 118
nu = 0.095509
obj = -1.382745, rho = -0.034047
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.078257
obj = -1.445877, rho = -0.068025
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 153
nu = 0.066699
obj = -1.496726, rho = 0.041953
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.055299
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.043397
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.034056
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.026726
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.020973
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.016459
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.012916
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.010136
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.007955
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.006242
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.004899
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.003844
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
..*..*
optimization finished, #iter = 464
nu = 0.003017
obj = -1.508690, rho = 0.064360
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.193718
obj = -1.308829, rho = -0.241637
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.174616
obj = -1.478186, rho = -0.259422
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 61
nu = 0.154469
obj = -1.666676, rho = -0.238547
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.139523
obj = -1.876082, rho = -0.259025
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.125915
obj = -2.080543, rho = -0.288967
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*..*
optimization finished, #iter = 372
nu = 0.107186
obj = -2.301706, rho = -0.335579
nSV = 16, nBSV = 4
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.......*
optimization finished, #iter = 840
nu = 0.090779
obj = -2.576136, rho = -0.336226
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
..*
optimization finished, #iter = 286
nu = 0.080926
obj = -2.905651, rho = -0.264445
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*..........*.*
optimization finished, #iter = 1182
nu = 0.073286
obj = -3.247908, rho = -0.240098
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*..*
optimization finished, #iter = 289
nu = 0.063498
obj = -3.592719, rho = -0.236561
nSV = 13, nBSV = 2
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*
optimization finished, #iter = 155
nu = 0.056049
obj = -3.999309, rho = -0.202906
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.*.*
optimization finished, #iter = 201
nu = 0.051367
obj = -4.376430, rho = -0.070384
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.2% (952/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.046705
obj = -4.617500, rho = -0.073202
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95% (950/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.039994
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.031386
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.024630
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.019329
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.015169
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.011904
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 132
nu = 0.009342
obj = -4.670907, rho = -0.040520
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 94.5% (945/1000) (classification)
*.*
optimization finished, #iter = 105
nu = 0.172347
obj = -1.164015, rho = -0.235390
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.150668
obj = -1.326447, rho = -0.232923
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.140000
obj = -1.511813, rho = -0.331063
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 55
nu = 0.135766
obj = -1.667197, rho = -0.581055
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.116284
obj = -1.790597, rho = -0.596459
nSV = 16, nBSV = 7
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 114
nu = 0.097037
obj = -1.926087, rho = -0.590888
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 120
nu = 0.082800
obj = -2.070807, rho = -0.557306
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.069312
obj = -2.225796, rho = -0.567545
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
.*..*.*
optimization finished, #iter = 437
nu = 0.057902
obj = -2.404895, rho = -0.546085
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.1% (961/1000) (classification)
*.*
optimization finished, #iter = 119
nu = 0.050405
obj = -2.598147, rho = -0.460877
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.045598
obj = -2.732790, rho = -0.277834
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.*.*
optimization finished, #iter = 273
nu = 0.038291
obj = -2.754217, rho = -0.161976
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.030048
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.023580
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.018505
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.014522
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.011396
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.008943
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.007018
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
..*
optimization finished, #iter = 270
nu = 0.005508
obj = -2.754216, rho = -0.162244
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.2% (962/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.186164
obj = -1.235235, rho = -0.164942
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 97
nu = 0.164640
obj = -1.396126, rho = -0.177614
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 86
nu = 0.147310
obj = -1.573236, rho = -0.144273
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.131004
obj = -1.758496, rho = -0.146435
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.115198
obj = -1.970607, rho = -0.091440
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.104352
obj = -2.186923, rho = -0.061872
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*.*
optimization finished, #iter = 133
nu = 0.092380
obj = -2.388087, rho = -0.124997
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.080440
obj = -2.570505, rho = -0.138678
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.066814
obj = -2.773536, rho = -0.107102
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
*
optimization finished, #iter = 53
nu = 0.058393
obj = -2.972382, rho = -0.102960
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 65
nu = 0.048493
obj = -3.171648, rho = -0.016263
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 107
nu = 0.040733
obj = -3.389936, rho = 0.093373
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.034263
obj = -3.625372, rho = 0.166176
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 130
nu = 0.030912
obj = -3.802109, rho = 0.207204
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.025673
obj = -3.821016, rho = 0.201179
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.020147
obj = -3.821016, rho = 0.201179
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.015811
obj = -3.821016, rho = 0.201179
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.012408
obj = -3.821016, rho = 0.201179
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.009737
obj = -3.821016, rho = 0.201179
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 213
nu = 0.007641
obj = -3.821016, rho = 0.201179
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 68
nu = 0.221049
obj = -1.496830, rho = 0.085206
nSV = 26, nBSV = 19
Total nSV = 26
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.198496
obj = -1.695795, rho = 0.103127
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.178302
obj = -1.915263, rho = 0.090416
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*..*
optimization finished, #iter = 210
nu = 0.157518
obj = -2.149914, rho = 0.076664
nSV = 22, nBSV = 13
Total nSV = 22
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 116
nu = 0.137088
obj = -2.426385, rho = 0.091953
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 128
nu = 0.129922
obj = -2.712785, rho = 0.304294
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*..*
optimization finished, #iter = 474
nu = 0.112155
obj = -2.964682, rho = 0.381141
nSV = 16, nBSV = 6
Total nSV = 16
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
....*.*
optimization finished, #iter = 578
nu = 0.094874
obj = -3.265233, rho = 0.347233
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*.*
optimization finished, #iter = 355
nu = 0.079984
obj = -3.635203, rho = 0.339266
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
..*
optimization finished, #iter = 295
nu = 0.068620
obj = -4.098351, rho = 0.326335
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 98% (98/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 132
nu = 0.061812
obj = -4.650439, rho = 0.274903
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 99% (99/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
...*.....*
optimization finished, #iter = 873
nu = 0.054898
obj = -5.240018, rho = 0.149123
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
..*
optimization finished, #iter = 287
nu = 0.050811
obj = -5.869508, rho = -0.017486
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*..*
optimization finished, #iter = 308
nu = 0.047973
obj = -6.368739, rho = -0.188552
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*
optimization finished, #iter = 187
nu = 0.039661
obj = -6.746597, rho = -0.170824
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.033150
obj = -7.180748, rho = -0.119927
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.030628
obj = -7.399608, rho = 0.138553
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
....*.*
optimization finished, #iter = 515
nu = 0.024035
obj = -7.399608, rho = 0.138403
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
....*.*
optimization finished, #iter = 515
nu = 0.018862
obj = -7.399608, rho = 0.138403
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
....*.*
optimization finished, #iter = 515
nu = 0.014802
obj = -7.399608, rho = 0.138403
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*
optimization finished, #iter = 66
nu = 0.217275
obj = -1.527055, rho = -0.064454
nSV = 27, nBSV = 18
Total nSV = 27
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 59
nu = 0.197027
obj = -1.762007, rho = 0.014756
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 93
nu = 0.180368
obj = -2.019982, rho = 0.090056
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 127
nu = 0.165069
obj = -2.295153, rho = 0.131826
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 280
nu = 0.145685
obj = -2.593068, rho = 0.158629
nSV = 20, nBSV = 10
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 199
nu = 0.125808
obj = -2.960074, rho = 0.159684
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.114772
obj = -3.396989, rho = 0.129654
nSV = 17, nBSV = 9
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 172
nu = 0.105795
obj = -3.857523, rho = 0.109239
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
..*
optimization finished, #iter = 286
nu = 0.097943
obj = -4.326617, rho = 0.124717
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.085014
obj = -4.793933, rho = 0.137115
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 250
nu = 0.072038
obj = -5.345324, rho = 0.129750
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
*.*
optimization finished, #iter = 172
nu = 0.061426
obj = -6.046423, rho = 0.123239
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*
optimization finished, #iter = 192
nu = 0.056898
obj = -6.842341, rho = 0.150213
nSV = 14, nBSV = 4
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 214
nu = 0.052149
obj = -7.622217, rho = 0.136798
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
.*
optimization finished, #iter = 170
nu = 0.045635
obj = -8.376680, rho = 0.136768
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 134
nu = 0.038621
obj = -9.241780, rho = 0.187634
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.037438
obj = -9.985216, rho = 0.501664
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.032804
obj = -10.104120, rho = 0.689332
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.025744
obj = -10.104120, rho = 0.689332
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
.*.*
optimization finished, #iter = 200
nu = 0.020203
obj = -10.104120, rho = 0.689332
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 67
nu = 0.214723
obj = -1.376355, rho = -0.083442
nSV = 24, nBSV = 16
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*
optimization finished, #iter = 73
nu = 0.183500
obj = -1.538257, rho = -0.082941
nSV = 23, nBSV = 15
Total nSV = 23
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 79
nu = 0.161569
obj = -1.732088, rho = -0.160324
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
*
optimization finished, #iter = 62
nu = 0.145310
obj = -1.941595, rho = -0.218431
nSV = 18, nBSV = 12
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
*.*
optimization finished, #iter = 159
nu = 0.128606
obj = -2.162711, rho = -0.237127
nSV = 18, nBSV = 10
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 98.6% (986/1000) (classification)
.*
optimization finished, #iter = 165
nu = 0.113118
obj = -2.395857, rho = -0.237833
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.7% (987/1000) (classification)
*.*
optimization finished, #iter = 135
nu = 0.102522
obj = -2.624788, rho = -0.009694
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*..*
optimization finished, #iter = 427
nu = 0.089433
obj = -2.822478, rho = 0.193038
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.079237
obj = -2.962732, rho = 0.427602
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 224
nu = 0.064925
obj = -3.048590, rho = 0.468238
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 167
nu = 0.054890
obj = -3.098112, rho = 0.389410
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.043073
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.033802
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.026527
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.020817
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.016336
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.012820
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.010061
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.007895
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 178
nu = 0.006196
obj = -3.098112, rho = 0.389411
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 51
nu = 0.224735
obj = -1.472860, rho = -0.245838
nSV = 25, nBSV = 20
Total nSV = 25
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 69
nu = 0.205551
obj = -1.631826, rho = -0.348445
nSV = 23, nBSV = 16
Total nSV = 23
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 71
nu = 0.176217
obj = -1.799413, rho = -0.357247
nSV = 20, nBSV = 13
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 151
nu = 0.153673
obj = -1.988831, rho = -0.289824
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.130925
obj = -2.203303, rho = -0.324840
nSV = 16, nBSV = 10
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 60
nu = 0.116223
obj = -2.441395, rho = -0.351718
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 95
nu = 0.103841
obj = -2.662587, rho = -0.440686
nSV = 13, nBSV = 5
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 139
nu = 0.088626
obj = -2.878343, rho = -0.483632
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.074302
obj = -3.120923, rho = -0.468669
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
.*.*
optimization finished, #iter = 256
nu = 0.064627
obj = -3.359314, rho = -0.422856
nSV = 12, nBSV = 1
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.054480
obj = -3.619449, rho = -0.336280
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.047701
obj = -3.861395, rho = -0.251017
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
..*
optimization finished, #iter = 279
nu = 0.041459
obj = -4.019247, rho = -0.137625
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.034605
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.027156
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.021311
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.016724
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.013124
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.010300
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 186
nu = 0.008083
obj = -4.041866, rho = -0.074900
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.177487
obj = -1.127518, rho = 0.190511
nSV = 22, nBSV = 15
Total nSV = 22
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 121
nu = 0.155062
obj = -1.247631, rho = 0.266388
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 57
nu = 0.133184
obj = -1.385490, rho = 0.246008
nSV = 18, nBSV = 11
Total nSV = 18
Accuracy = 100% (100/100) (classification)
Accuracy = 97% (970/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.120006
obj = -1.531963, rho = 0.284682
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.105986
obj = -1.667658, rho = 0.336667
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*.*.*
optimization finished, #iter = 309
nu = 0.088492
obj = -1.808961, rho = 0.355785
nSV = 15, nBSV = 5
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 147
nu = 0.078585
obj = -1.949050, rho = 0.443339
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 149
nu = 0.067763
obj = -2.068533, rho = 0.452196
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.059258
obj = -2.148435, rho = 0.452135
nSV = 10, nBSV = 2
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.048366
obj = -2.164340, rho = 0.457685
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
..*.*
optimization finished, #iter = 320
nu = 0.038114
obj = -2.176120, rho = 0.462815
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.3% (963/1000) (classification)
....*
optimization finished, #iter = 490
nu = 0.030258
obj = -2.188130, rho = 0.457521
nSV = 9, nBSV = 1
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.023874
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.018735
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.014703
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.011538
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.009055
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.007106
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.005576
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.....*.*
optimization finished, #iter = 606
nu = 0.004376
obj = -2.188484, rho = 0.448143
nSV = 9, nBSV = 0
Total nSV = 9
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
*.*
optimization finished, #iter = 101
nu = 0.171806
obj = -1.084049, rho = -0.038761
nSV = 21, nBSV = 13
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*.*
optimization finished, #iter = 169
nu = 0.144930
obj = -1.206024, rho = -0.048577
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 52
nu = 0.128250
obj = -1.354659, rho = -0.014396
nSV = 14, nBSV = 9
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.110643
obj = -1.521207, rho = 0.005582
nSV = 14, nBSV = 8
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 70
nu = 0.100661
obj = -1.704734, rho = -0.077078
nSV = 13, nBSV = 7
Total nSV = 13
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*
optimization finished, #iter = 94
nu = 0.091907
obj = -1.883075, rho = -0.146949
nSV = 12, nBSV = 6
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*
optimization finished, #iter = 191
nu = 0.078507
obj = -2.053514, rho = -0.021414
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
.*
optimization finished, #iter = 135
nu = 0.066108
obj = -2.250418, rho = -0.005365
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.056290
obj = -2.489300, rho = -0.053658
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.5% (975/1000) (classification)
*.*
optimization finished, #iter = 122
nu = 0.050428
obj = -2.736884, rho = 0.066055
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*
optimization finished, #iter = 98
nu = 0.044031
obj = -2.975530, rho = -0.021695
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 115
nu = 0.037078
obj = -3.231308, rho = -0.087629
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 122
nu = 0.032330
obj = -3.514643, rho = -0.205641
nSV = 7, nBSV = 1
Total nSV = 7
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 152
nu = 0.029756
obj = -3.716085, rho = -0.426092
nSV = 8, nBSV = 1
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.6% (966/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.025154
obj = -3.743538, rho = -0.543041
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.019740
obj = -3.743538, rho = -0.543041
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.015491
obj = -3.743538, rho = -0.543041
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.012157
obj = -3.743538, rho = -0.543041
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.009540
obj = -3.743538, rho = -0.543041
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
.*
optimization finished, #iter = 169
nu = 0.007487
obj = -3.743538, rho = -0.543041
nSV = 8, nBSV = 0
Total nSV = 8
Accuracy = 100% (100/100) (classification)
Accuracy = 96.5% (965/1000) (classification)
*.*
optimization finished, #iter = 146
nu = 0.180610
obj = -1.198455, rho = -0.240458
nSV = 22, nBSV = 14
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*.*
optimization finished, #iter = 158
nu = 0.159199
obj = -1.352617, rho = -0.309790
nSV = 20, nBSV = 12
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*..*
optimization finished, #iter = 214
nu = 0.138510
obj = -1.529805, rho = -0.389186
nSV = 19, nBSV = 9
Total nSV = 19
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 88
nu = 0.121255
obj = -1.749225, rho = -0.331938
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.111062
obj = -1.997130, rho = -0.512714
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.099295
obj = -2.271015, rho = -0.401252
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 141
nu = 0.088338
obj = -2.587594, rho = -0.284982
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 92
nu = 0.079093
obj = -2.953957, rho = -0.298853
nSV = 12, nBSV = 4
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
*.*
optimization finished, #iter = 124
nu = 0.072131
obj = -3.351281, rho = -0.319523
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 112
nu = 0.065952
obj = -3.763988, rho = -0.292344
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
*.*
optimization finished, #iter = 115
nu = 0.061868
obj = -4.133001, rho = -0.251584
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.8% (958/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.055544
obj = -4.365179, rho = -0.177475
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 100% (100/100) (classification)
Accuracy = 96% (960/1000) (classification)
..*.*
optimization finished, #iter = 377
nu = 0.048536
obj = -4.488183, rho = -0.192474
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 95.7% (957/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.038496
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.030210
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.023708
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.018605
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.014600
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.011458
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
.......*
optimization finished, #iter = 796
nu = 0.008992
obj = -4.496217, rho = -0.203951
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 95.9% (959/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.209866
obj = -1.462149, rho = -0.163788
nSV = 24, nBSV = 18
Total nSV = 24
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
*
optimization finished, #iter = 31
nu = 0.194672
obj = -1.662588, rho = -0.120900
nSV = 22, nBSV = 16
Total nSV = 22
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 100
nu = 0.175044
obj = -1.874865, rho = -0.088472
nSV = 20, nBSV = 14
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*.*
optimization finished, #iter = 113
nu = 0.152784
obj = -2.111476, rho = -0.064965
nSV = 19, nBSV = 10
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 98% (980/1000) (classification)
.*.*
optimization finished, #iter = 221
nu = 0.132359
obj = -2.396451, rho = -0.043332
nSV = 18, nBSV = 9
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 96
nu = 0.115685
obj = -2.749807, rho = -0.046107
nSV = 16, nBSV = 8
Total nSV = 16
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.105687
obj = -3.166481, rho = -0.084706
nSV = 15, nBSV = 8
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 166
nu = 0.098538
obj = -3.616638, rho = -0.153930
nSV = 14, nBSV = 7
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*
optimization finished, #iter = 143
nu = 0.091755
obj = -4.067746, rho = -0.306462
nSV = 13, nBSV = 6
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*..*
optimization finished, #iter = 375
nu = 0.080369
obj = -4.493733, rho = -0.371275
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 96.7% (967/1000) (classification)
..*..*
optimization finished, #iter = 441
nu = 0.069163
obj = -4.986868, rho = -0.403112
nSV = 11, nBSV = 3
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*..*
optimization finished, #iter = 308
nu = 0.065920
obj = -5.433029, rho = -0.581743
nSV = 10, nBSV = 3
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
...*...*
optimization finished, #iter = 696
nu = 0.059750
obj = -5.617761, rho = -0.693923
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.048244
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.037860
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.029711
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.023316
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.018297
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.014359
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
....*.....*
optimization finished, #iter = 903
nu = 0.011268
obj = -5.634469, rho = -0.643599
nSV = 11, nBSV = 0
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 98.1% (981/1000) (classification)
*
optimization finished, #iter = 48
nu = 0.198078
obj = -1.431489, rho = 0.077028
nSV = 25, nBSV = 18
Total nSV = 25
Accuracy = 98% (98/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 28
nu = 0.184769
obj = -1.657721, rho = -0.000253
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*
optimization finished, #iter = 45
nu = 0.163977
obj = -1.914981, rho = 0.071979
nSV = 19, nBSV = 14
Total nSV = 19
Accuracy = 99% (99/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
*
optimization finished, #iter = 27
nu = 0.147894
obj = -2.224902, rho = 0.163916
nSV = 18, nBSV = 13
Total nSV = 18
Accuracy = 99% (99/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
*
optimization finished, #iter = 29
nu = 0.139071
obj = -2.579016, rho = 0.026194
nSV = 17, nBSV = 11
Total nSV = 17
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.131867
obj = -2.932558, rho = -0.079433
nSV = 15, nBSV = 9
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.118331
obj = -3.293997, rho = -0.072675
nSV = 16, nBSV = 9
Total nSV = 16
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
.*.*
optimization finished, #iter = 291
nu = 0.105722
obj = -3.672539, rho = -0.067047
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 100% (100/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
*.*
optimization finished, #iter = 191
nu = 0.098353
obj = -4.007864, rho = -0.061611
nSV = 14, nBSV = 5
Total nSV = 14
Accuracy = 100% (100/100) (classification)
Accuracy = 97.9% (979/1000) (classification)
...*.*
optimization finished, #iter = 409
nu = 0.084978
obj = -4.251225, rho = -0.074521
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.8% (978/1000) (classification)
..*.*
optimization finished, #iter = 322
nu = 0.069366
obj = -4.513587, rho = -0.089275
nSV = 13, nBSV = 3
Total nSV = 13
Accuracy = 100% (100/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 184
nu = 0.058849
obj = -4.820692, rho = -0.200784
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.6% (976/1000) (classification)
.*.*
optimization finished, #iter = 207
nu = 0.051354
obj = -5.037481, rho = -0.383241
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*
optimization finished, #iter = 176
nu = 0.041245
obj = -5.218401, rho = -0.369809
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 103
nu = 0.034905
obj = -5.383268, rho = -0.216749
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.028457
obj = -5.396589, rho = -0.136294
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.022332
obj = -5.396589, rho = -0.136294
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.017525
obj = -5.396589, rho = -0.136294
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.013753
obj = -5.396589, rho = -0.136294
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*.*
optimization finished, #iter = 141
nu = 0.010793
obj = -5.396589, rho = -0.136294
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.3% (973/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.208365
obj = -1.441447, rho = 0.007191
nSV = 24, nBSV = 17
Total nSV = 24
Accuracy = 99% (99/100) (classification)
Accuracy = 99% (990/1000) (classification)
*
optimization finished, #iter = 39
nu = 0.194066
obj = -1.642082, rho = 0.115934
nSV = 21, nBSV = 16
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 99.1% (991/1000) (classification)
*
optimization finished, #iter = 85
nu = 0.172097
obj = -1.839073, rho = 0.140875
nSV = 21, nBSV = 12
Total nSV = 21
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 89
nu = 0.147142
obj = -2.080761, rho = 0.140967
nSV = 20, nBSV = 11
Total nSV = 20
Accuracy = 99% (99/100) (classification)
Accuracy = 98.9% (989/1000) (classification)
*
optimization finished, #iter = 64
nu = 0.132153
obj = -2.370100, rho = 0.138866
nSV = 17, nBSV = 10
Total nSV = 17
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
*
optimization finished, #iter = 74
nu = 0.118126
obj = -2.691770, rho = 0.099538
nSV = 15, nBSV = 7
Total nSV = 15
Accuracy = 99% (99/100) (classification)
Accuracy = 98.8% (988/1000) (classification)
.*
optimization finished, #iter = 163
nu = 0.104493
obj = -3.062061, rho = 0.116114
nSV = 17, nBSV = 7
Total nSV = 17
Accuracy = 98% (98/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
.*.*
optimization finished, #iter = 238
nu = 0.091980
obj = -3.495431, rho = 0.113769
nSV = 15, nBSV = 6
Total nSV = 15
Accuracy = 98% (98/100) (classification)
Accuracy = 98% (980/1000) (classification)
*.*
optimization finished, #iter = 102
nu = 0.083842
obj = -4.002084, rho = 0.104028
nSV = 13, nBSV = 4
Total nSV = 13
Accuracy = 98% (98/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
*.*
optimization finished, #iter = 109
nu = 0.074589
obj = -4.555480, rho = 0.085194
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.5% (985/1000) (classification)
.*
optimization finished, #iter = 195
nu = 0.065014
obj = -5.235900, rho = 0.066988
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.4% (984/1000) (classification)
.*.*
optimization finished, #iter = 216
nu = 0.057775
obj = -6.085604, rho = 0.040119
nSV = 12, nBSV = 3
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.2% (982/1000) (classification)
..*...*
optimization finished, #iter = 588
nu = 0.052778
obj = -7.086856, rho = 0.027053
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 98.3% (983/1000) (classification)
...*
optimization finished, #iter = 384
nu = 0.048241
obj = -8.272231, rho = 0.042162
nSV = 12, nBSV = 2
Total nSV = 12
Accuracy = 99% (99/100) (classification)
Accuracy = 97.7% (977/1000) (classification)
.*
optimization finished, #iter = 183
nu = 0.044720
obj = -9.665879, rho = -0.069870
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.4% (974/1000) (classification)
.*.*
optimization finished, #iter = 230
nu = 0.043666
obj = -11.094872, rho = -0.308762
nSV = 11, nBSV = 2
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
..*.*
optimization finished, #iter = 310
nu = 0.040194
obj = -12.383723, rho = -0.541624
nSV = 11, nBSV = 1
Total nSV = 11
Accuracy = 99% (99/100) (classification)
Accuracy = 96.9% (969/1000) (classification)
.*.*
optimization finished, #iter = 275
nu = 0.036258
obj = -13.657952, rho = -0.896028
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.1% (971/1000) (classification)
.*.*
optimization finished, #iter = 269
nu = 0.033265
obj = -14.694549, rho = -1.350474
nSV = 10, nBSV = 1
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 97.2% (972/1000) (classification)
.*.*
optimization finished, #iter = 279
nu = 0.030146
obj = -15.072156, rho = -1.836574
nSV = 10, nBSV = 0
Total nSV = 10
Accuracy = 100% (100/100) (classification)
Accuracy = 96.8% (968/1000) (classification)
No description has been provided for this image